{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.预处理与缩放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iBMuWLSMmJoaIiAhee+0170+QmQlXXAH79kmPXMHvl54OkyfDW29B795V/WspVaP4vJ6pGkMDK1VnTJw4kaVLl9KiRQs2bNgAwMGDB7n66qvZvn077dq145133uHEE08s9d6EhATuuOMOHA4HN954I9OmTfN38ZVS9Vl8vPRQubNlC6SmQvPmVXrac845h7KWXTHG8Pzzz1fsBEuWwJEjElSVlJUFjz8uwZVSdZjP65mqMXQooKozJkyYQEJCQrHX5s6dy6BBg0hOTmbQoEHMnTu31PscDgdTp05l+fLlbNq0iUWLFrFp06ZS+ymllM+kpXmedxQUJL0/tc3HH8uwRnfWrNH5VkqpOkMDK1VnDBgwgCZNmhR7LT4+nvHjxwMwfvx4Pvroo1LvS0xMJCYmhvbt2xMSEsKYMWOIj4/3S5mVUgqA2FiIjHS/PSAATjrJf+WpKq56qooqoxVfKaVqEw2sVJ22d+/e45M/o6Ki2LdvX6l9UlJSaNOmzfHn0dHRpKSkuDxeXFwcsbGxxMbGkpqa6ptCK6Xqn4suguBg19vCwmDCBAgJ8WuRqsTQoeApc1mPHhI0KqVUHaBXM1XvuRr3bIxxuW/RNKfNq3iug1KqHgsLgzffhMaNISJCXgsIkKBk4EC4445qLV6FXXEFhIaCq2tqWJhkCVRKqTpCk1eoOq1ly5bs3r2bqKgodu/eTYsWLUrt4+3aEUop5VOnnQarVsEnn0BiIjRsCKNGQbdu1V2yimvYEN57T1KsHzgg86kCA2UI4OzZcM451V1CpZSqMhpYqTpt5MiRvPHGG0ybNo033niDUaNGldqnX79+JCcns23bNlq3bs3ixYt5S7NUKaWqQ1iY9PJccUV1l6TqdOgga1YlJsoiwY0bwwUXyO+qlFJ1iA4FVHXG2LFjOfPMM9m8eTPR0dG88sorTJs2jc8//5xTTz2Vzz///Hga9b/++osRI0YAEBQUxLx58xg2bBhdunRh9OjRdO3atTp/FaWUqluMkUWBr7kGRozQoEopVSeZMvLqa7oepdyIjY0lKSmpuouhVE3merJi9dHvND/R66NfaT2rp7Se+ZVX9Ux7rJRSSimllFKqkjSwUkoppZRSSqlK0sBKKaWUUkoppSpJAyullFJKKaWUqiQNrJRSSimllFKqkjSwUkopX8rKgj17IDu7ukuilFJKKR/SBYKVUsoXDhyA2bNh+XIICABr4eKL4cEHZYFUbx08CKtXy/tPPx2aN/ddmZVSSilVYRpYKaVUVUtLg1GjYO9eyMsrfH3JEkhKgk8+gchIz8dwOGDmTHj7bQgJkddyc+W4//wnBAf7rvxKKaWUKjcdCqiUUlXtjTdg//7iQRVIYLR3LyxeXPYxZs2C996DnBw4dkwe2dkSnN1/v2/KreqdiRMn0qJFC7p16+Zy+4oVK2jUqBG9evWiV69ezJo1y88lVKr203pWf2hgpZRSVe3tt93PqcrKgkWLPL//0CEJvjIzXb//449h377Kl1PVexMmTCAhIcHjPueeey7r1q1j3bp1PPzww34qmVJ1h9az+kMDK6WUqmoZGZ63p6d73p6YWDj8z5XgYFi5svzlUqqEAQMG0KRJk+ouhlJ1mtaz+kMDK6WUqmpdu7rfZgx0716541srD6X8YNWqVfTs2ZPhw4ezcePG6i6OUnWS1rO6QZNXKKVUVZs6FdaudT2ULywMpkzx/P5+/WRulTsOB5x9duXKqJQX+vTpw44dO2jQoAHLli3j0ksvJTk52eW+cXFxxMXFAZCamurPYipVq2k9qzu0x0oppapa//5w990QGlqYvS8kRJ4/8AD07u35/U2awFVXQXh46W1hYTBiBLRsWfXlVqqEhg0b0qBBAwBGjBhBbm4u+/fvd7nvpEmTSEpKIikpiea6LIBSXtN6Vndoj5VSSvnCjTfChRdKEoodO6B9e7j6amjVyrv3z5gB+fnw/vuFwVlurgRVjz7qs2IrVdSePXto2bIlxhgSExPJz8+nadOm1V0speoUrWd1hwZWSinlK9HRcO+9FXtvUBA88oj0fK1aJXOq+veHFi2qtoyqXhs7diwrVqxg//79REdHM3PmTHJzcwGYPHky7733Hi+88AJBQUGEh4ezePFijDHVXGqlahetZ/WHsZ4nQOvsaKXciI2NJSkpqbqLoVRNVtPuDPQ7zU/0+uhXWs/qKa1nfuVVPdM5VkoppZRSSilVSRpYKaWUUkoppVQlaWCllFJKKaWUUpWkgZVSSimllFJKVZIGVkoppZRSSilVSZpuXSmllFJ1Q14ebNsGxsjacQHafqyU8h8NrJRSSilVu1kLr78Ozz4LOTnyWng4/P3vMHp0tRZNKVV/aGCllFJKqdrt3/+GuDjIzCx8LSMDZsyQfydMqK6SKaXqEe0jV0oppapCRga8+SaMGgVDh8L06bBjR3WXqu5LS4MXXyweVBXIzITHH4esLP+XSylV72hgpZRSSlXWwYMwfDjMmQP/+x8kJ8OiRfLaihXVXbq6bcUKCPIwACcgANaskZ8zM+E//4Ezz4TOneGCC2DxYsjP90tRlVJ1mwZWSimlVGU98ADs3l281yQ3V55PnQrHjlVf2eq6rKyyA6PMTHlccQU89xzs2QPZ2ZLoYtYsmDJFgyulVKVpYKWUUkpVxpEj8PXXEki58/HHVXOuTZtg7lx46CGIj5fgoL7r1UuSV7iTkwM9esCrr8LWraWHBWZmwvffw5df+racSqk6TwMrpZRSqjL++gtCQtxvz8iQG/rKyMuDW26RHpeXXoKFC+H//g/OPluGHdZnHTtK4BQcXHpbSAicey60aiV/M3eBaEYGzJ/v23Iqpeo8DayUUkqpymjWrDDFtyuhoXDSSZU7x1NPSa9Y0WFv6elw4ACMHev5/PXBiy9KgBURIc+NkZ+7d4dnnpHXDh3yfIzdu31bRqVUnafp1pVSSqnKaN4cevaEpCT3Q9JGjqz48bOzpTfFXWa7rCz47DO4+OKKn6Omy8yEX36Rn3v0gLCw4ttPPFGGWyYmwjffSMKKQYNkmKAxsk9UFGzf7vr4xkBMjM+Kr1S1s1aGK3vqXVeVpoGVUkopVVlz58Jll0kvksNR+Hp4ONx3nwRfFbVzZ/FjlpSeLkFdXQysrJU1ql56SYIlkB67yZPh1lsLgyaQn884Qx6u3HQT/POfrtOyh4XBDTdUffmVqm5padJr+847MuS1USO47joZWhwaWnr/PXtkse1PP5Xnw4bB9ddDy5Z+LXZtpUMBlVJKqcpq3x6WLYPRo+GEE+SGJTYW/vtfuSmpKGslPbindZgCA6FBgwodfuLEibRo0YJu3bq5Ob3l9ttvJyYmhh49evDTTz9V6DwV9thjsvBvRoZkVjx2TH5+4QV48snyHevqq+GccwqHC4IEY+Hh8n/Ur1/h66tXSyA2aBCMGyc9gpo1UFVQtdWzo0elt3zhQmmAsRYOH5Y6dc01pRPubNwIQ4bAa69J7+727fLz4MGSOEeVSQMrpZRSqiq0bg2PPCLrWP32G7z7riROqIwvvoCEBM/7BAfLzdDmzZJIoxwmTJhAgofjL1++nOTkZJKTk4mLi2PKlCneHTgrC956C95/X+aBVcSRI3JT527h35dfltZ4bwUGylysp5+Gs86SYHjIEGmdv+++wv0eeQQmTpS//dat8MMPcNdd0sKvwZWqAJ/Vs7K8+qrMHSwZQGVlwa+/wtKlha9ZCzffLI0XReds5uTIazff7Dn7pgI0sFJKKaVqrrg414FFAWMkMcaYMZIx8IILYMQI+Plnrw4/YMAAmjRp4nZ7fHw81113HcYY+vfvz+HDh9ntKcnD4cPSa/fHHzB7Njz8sGQufOSR8t+Uff+954V/g4Nh1aryHTMgAIYOhTfflPTq//0vnH564fbVq6V1v+TfPCMDvv0W3nuvfOdTCh/UM28tWuQ+sU1mJixYUPh87Vqpv+4cPOj1daU+08BKKaWUqqm2bfO83Rhpkc7KkqE+2dnSEj1uHKxfX+nTp6Sk0KZNm+PPo6OjSUlJcblv3H//S2y7dsQuWUJqbq6UKSNDyrRwITz/fPlO7mldMCicjF+VXn7ZfSCbmSmBrlJVrFz1LC6O2NhYYmNjSU1N9Xzgo0c9bz94sPDnP//0vK8xZe+jNLBSSqlaLT8f9u0r35AoVXs0bep5u7Wu12bKzIQ5cyp9euuil8kUTRhRxKR+/Uhq3ZqkqCiaBwaWLs+LL5ZvQePYWFm/y53cXOjb1/vjeeOPPzxvL+dQS6W8Ua56NmkSSUlJJCUl0byspDinnOJ+mzHQpUvhc2+WhKjsshH1gAZWSilVG+XnS+t5v34wYIDchF51lczvUXXHhAmSXMGVwEDPw+t+/lnmRlRCdHQ0O3fuPP58165dtGrVyvXO33/vOcmGMbBlS3lOLp9tV5nLQkMlsURUlPv3Z2dDamr51vgq68bxxBO9P5ZSXipXPSuPyZPdXz/CwmDSpMLnZ5zhfl+QbUWHzSqXNLBSSqnaaNo0SaF78KDcQObmSsrtMWNg3brqLp2qKldcIa3KJddtCguDhg09vzcgoHw9RC6MHDmS+fPnY61l9erVNGrUiCh3wUxZgZ61sk95PPOM3PCFhcn6OyEh8vOZZ7rPCnjgANx9t6wtdu65spbVQw+VPSwKJJAtmjWwqLAwSVOtVBUrVz0rj4sugiuvlKCoYLmCwED5LN95J/TuXbhvYCDMmyf7Fq2ngYHy2rx5hcdQbuk6VkopVVlZWTIZfsECOHQI2raVDEojRhRfZ6eq/P67LIbqqncgMxP+7//gk0+q/rzK/0JCJLve66/LY/9+aNZMAoDQUHj8cZnH5ErDhmX2sIwdO5YVK1awf/9+oqOjmTlzJrnOeUuTJ09mxIgRLFu2jJiYGCIiInjttdfcHywiwnNgFRgIHTt6LI/LY77xhvR0ffedvDZgAJx6quv9jxyBSy6RnqqiwwjfeQfWrIElS0oHqUUNGSLJNr7/vvhcq7AwWUB4/Pji++fmSjZGY+R3Cw4u3++n6oUqrWflYQzMmiVr7C1cKGvidewoDQSu6uIZZ0gdef55+Ppree3882HqVPn8//GHDD1v00Z6lFUpxtW4ziI0r6JSbsTGxpKUlFTdxVDVLTNTWgS3bi0e6EREyPohjzxS9cHVE0/IfBVPi8b27i2L1pb3RrZq+SCqrJS69Z127JgEAa7m14WHw9//LgGYv1x00fG1bmL/+oukkkOZzjtPgkNfeuopGSLrqqcuPFwaHcaN83wMh0NS5cfFSWKQE0+UG9EJEwqDMmsl0cVzzxWmYA8MlF6ACRN806DintazesrtfUh6Onz0EXzzjTTAjBolAVJ5e4wLbNokn+2dO6XxIDNThuHecov0qtePBgWv6pkGVkpVkAZWCpChSu4m5YeHyzoi/ftX7TkffFB6yMoSGSlfrjExVXt+7+kNn69t2AB/+5v0nKSnS3rywEBJeT5zpn9v8Dt0OB5kuAysevaUz6MvnXkm7NnjfnuXLrKQc2U9/TS89FLpDILh4XDrrXLD6T9az+opl/chW7bIYtg5OYW92ZGR0KqV9Eh99ZU0DAwaJItgn3yy55Ps2CGNJunprrc3bCiNEGecUXaBk5Nljb+GDaWuhoSU/Z6aw6t6pkMBlVKqMhYudD+PJStLWuiPHJGW7S1bpMV71Ci5+WrZsmLn7N1bblDdfdEVyMiARx+FV16p2HlUzdetm6y9lJAgiUsaNZKe0nbtPL8vL09aofPyJNjwNGndW0FBnhNFVMU5ylJWnfC0To+30tJk/St32Rife06GDEZGVv5cSpWHwyENLSU/5+npEtQkJxe+tnChrMu2cGHx7JppafDTT9Io07o13HCD53qVlgbXXitDlvv1c73Pvn2SKGPzZmn4KWjw+ec/5fuwDtFZaEopVRmHDrnfZq0sunjXXbBxo/QqHD0KixfD8OGwa1fFznnRRZ4XTi16/m+/rfhaP9ZK6/+uXZ6HHarqVTDU56GH4Pbbyw6qFi2SG6Bx4yQA6NsXHnus8v/HQ4a4n9weESFZK32tc2fP2/fulb9VZRK8fPed5/oXFAQrV1b8+EpV1IoVZTcuFJWVBddcI+/Jy5Ne7mSh26YAACAASURBVH79YMoUmSc8ZIh3a1fl5UnipHffLb0tJ0eGC27YULje3rFj8pg2TYYr1iEaWCmlVGV4WkfEGMnaV3K4UF6etPLNmFGxc4aFSaKMhg29y9JUnnTTBb7+Gi64AAYOhKFDJc3u6697Tk6gar4FC2D2bGnRLri5ycyU/9uHH67cse++23WvVHCwpDG/6KLKHd8bU6d67hnLz5eevWuuqfgCytnZZWc/rEidU6qyfvvN/QLX7uTkyDzhBx6A+fPleVZW+TOK5udLoPThh8VfT0iQBkhXDTdZWTIXuA7RwEoppSqj6IT2kooOeSjJ4ZCW7/KuM/T553DppTKGPihIsjN5Cq6aNnWfPtqdhASZI7J9u3y5ZmZKgPjYY5KFTtVOOTny/+fqxiszE95/X5I1VFT79pJ9r3t3+dw3aCC9aYMHwwcfuF6Pqqqdd560toeGep6on5kprfMV0aeP5969vDxJ8a6UvzVsWLF5S7/9Jr1NBYlYKio/H+67T+YgFkhI8NyL9vvvlV5vrybRwEoppSpj4kTo0aN0K3l4uKx67+kGLDBQ5l956+mn4Y47pKW9INhJSXHfeh4YKHNwUlO9P0d+vgwpc5fK/dVXy3c8VXP8/LPnnhZjZGJ7ZZx2mqRr7thR5lysXg3/+Y/M/fKX226TBBVlDQvcsKFic67atZMeXFc3sCEhcM45mopaVY8LL6x8cFRZDockdklMlOdljXKoY6MgNLBSSqnKCAmRyb+zZ0uA1aqVrLPz0ktlz3WxVtYkcmfTJvj0U7kh/v13ybzkalhhQEDhIo5FFfSKDRgg82q88csvnoeSGCO9Zqr2yc31nCUwP7/qhrCFhEjPVePGpbdZKy3UroL3qtK+fdlLDeTlwRdfVOzGbt48abQoWHg1IEB+7t1bMoUq5UvWyndCZmbx3p7mzSUxkj8SxXiSlSXLEQAMG+Y5kUuHDtK7XUdoYKXqvM2bN9OrV6/jj4YNG/JMiS++FStW0KhRo+P7zJo1q5pKq2ql4GCZnBsfL5PW33hDUsmuXev5fQ0buh7H/vvvMmn4yivh3nsl49Jll7lPQuFwyI3dmDGlhz/l5Mg5Zs2STE9lSU/3PLQwL698k6NVzdG9u+fAKTDQfVavqvLhhzJvr3dvCUyuvloaDnzh9NM9D4O1FqZPl17g8rbyN2woGdXeekvef+ed8PbbkpimDt0kqhrom2+ksWzkSFk/MTZW1mcr+C657TaYM0eGiXuT5MgXrC3MQDh8uNQXV0Nzw8Jkvb06RAMrVed16tSJdevWsW7dOtauXUtERASXXXZZqf3OPffc4/s9XNlJ3Ert3Vu4hog7qalw+eXFe4hSUyWg+uOPwtbIjAz519OwwoAAeY+7G8TsbBmSVZbOnT3ffBf0RKjap1EjmZ/nak5gcDB06iTBjq8895zcAP75pwToDocMFxo3ToYMVrWRI8u+sczIkF6r+PjyH98YmUt1++1yM6v1QvnaypUwebJkas3MlOt9drbMYbzxxsLe18sukwDsyiurb62ogtEYoaEyf7NzZ+lJi4iQxofISBnpMWhQ9ZTPRzSwUvXKl19+SYcOHWjbtm11F0XVdcHBZbeCWwt//SUt3wXmz5cvzPIOTwoIkKGD7t5nrXdZ0Jo0kaEbrr6MAwJk7S1vFoJUNdOsWdJjFBYmn9HAQLnR6dxZ5s/5SmoqPP+8+8QZ06ZV/VyLiAgZptuokede2MxMGWarVE03c6brIbRZWTIioegyAjk50mBQHRkqQ0JkKYcCUVGwdKl8182aBU89JSM6rrzS/2XzMQ2sVL2yePFixo4d63LbqlWr6NmzJ8OHD2fjxo1+Lpmqc5o1A28C+MzM4vOfli0r/xdhcLBkKisr9bq34+4ffVRa3yMiCufkREZKyuz58z3P01GVk5sLO3ZIj6cvhITACy/ITc6990qK9IUL5QbsxBN9c06A5cs9f2727ZMhsFWte3dYtars5BkVXVNOKX/Zt0+uDe5kZUm9LlCdSYbCw2UIYEmnnSbD5ocM8U+W0GpQTYMvlfK/nJwclixZwqOPPlpqW58+fdixYwcNGjRg2bJlXHrppSQXXaHcKS4ujjhny2aqZkZTZXnoIVltvqxJ+kePFv7szSKtgYGF+xkjw6p+/ln+dSc01PsFWiMiJPVuYqKkys3OhnPPlbTZwcHeHUOVT36+BDxxcYXD5Nq2lbXOzjyz6s/XoYM8/OXIEc/r4gQFlS9DZnkUZOj0tJh306a+ObdSVSU72/MSAvn5xYefN2zo+TsBin+XVKWmTT2XtQ7THitVbyxfvpw+ffrQsmXLUtsaNmxIA+eE4xEjRpCbm8v+/ftL7Tdp0iSSkpJISkqiuaeFYZUCCUbmzfMcjAQEQM+ehc/LStPcsydccgmcfLK811p5ZGS47+kq6Ck4etT7dYqMkSF/06fDI49I66MGVb4zbZoMlUtLk//L7GzYskXS+a9cWd2lq7zOnT0nksjJkUx+vnL99e7PHxYG113nu3MrVRWiojz38kRGwtlnFz5v2FAaZdyNZAgJkUW7C+ZWejsXq6wRC8ZATIx3x6qDNLBS9caiRYvcDgPcs2cP1jm+PzExkfz8fJpqC6aqCoMGSep1d1+IISFw882Fz7dv93y8xo1lPauLLy57Yn7BdmvlRv2NN+CCC+Cjj7wuvvKDrVtl7SdX84+ysuDBB2v/Wi/nn+9+KGpIiNSTJk18d/7hwyVLYMkyhIXJjeW4cd4d5+hRGZJVVk+AUlUtKEi+K1zVI2MksBo6tPjrM2dKooiSwVVIiBzviy9knu+WLd6Xo0MHmWvrLsAKC5NEGvWUBlaqXsjIyODzzz/n8ssvP/7aiy++yIsvvgjAe++9R7du3ejZsye33347ixcvxug8ElVVzjtPMoeFhhb2+oSEyPPp04tnE9u3z/OxCubefPqp57lYoaGlA6+cHLlRv/9+ySCoaoZlyzwPx9mzB3bu9F95fCEoCBYskIaBoj1HERHSuj13rm/PHxgoDRwPPCBDLENDZc25u+6SNOllzffYvBmuuQb69pX63Lcv/PvfGmAp/7r5ZpmjVPS7JDJSAp3Fi0v3OrVrB598AqNGyXuMIatFa9480oOpO4fyfynnsvFAhHw3OBxl90YFB8uQ8B9+gBEjitdlYyTou/FG3y/bUIMZ67kVrJY3kSnlO7GxsSQlJVV3MVRtsmOH3MT9+Se0bi1Dvj79VP7t3FkWdrz3XjhwwP0xBgyQnqfBgyseHAUGwujRMsTPt2pa60TN/E579FHPWekaNIB33oEuXar81AkJCdxxxx04HA5uvPFGpk2bVmz7ihUrGDVqFKeccgoAl19+uVfLUbi9Ph47JmtZffONtGxfeqn0ZtXk+RibN8uyCCWXTwgLkyDrhReqO6GL1rNaoErr2q5dsGwZsbNnk/T665Lp04s6tH7dIQaf/jFZeYZjNoxAHIQYB1dG/MJrzd4hMCRI5mq5a+gJDZVeruho6UX/5ht4/XVp/ImJkaHLffp4/0epXbyqZ5q8Qiml/KVtW1kMcd8+mSd16FDhor9r18Itt0CPHjLcyFVvVERE4VyQYcOkBd7dosGeOBy+W5RVlV/v3tLq7G7hZYdDWp6rmMPhYOrUqXz++edER0fTr18/Ro4cyWmnnVZsv3PPPZelRbONlcVaOHwYLrxQhrZGRkogf/PN0mP1t7/Jo7aYPdv1mnRZWfDtt5Liundv/5dL1RpVXteioyUxUlyc1+tAZWY6uOD8FRzMDaVgwJqDQDJtIG+n9ySQfF5q/gFB11wNa9ZI8FaQcCYwUHqrZs4snAdsjAR0Awd690eoJ3QooFJK+dvMmdIrVTIoysyUm7QWLUoP6QgPl4nJ558vz8ePL3uOlSeNG1f8vapqDR5cPLV9UWFhcPXV3qfKL4fExERiYmJo3749ISEhjBkzhviKLJRb0sMPQ0qK9PRkZ8PBg7JG1kUXyc+1SXq6ZMd0Jzu7+Dp0Srngs7pWDm+/vZOc3Hxc3frnEMTr6bG02PEgy5JPkOURHntMhvR17CgLDn/4oTSQKI80sFJKKX/KzJShFO6GWuTnS2/UTTfJZP6gIGkhvP9+GXJUMAm5RQvJ5FQRERHeT9ZXvhcUJGuZNW0qvTsg/8/h4XDWWfJ/7wMpKSm0adPm+PPo6GhSUlJK7VeuNf7Wr4f33y+9OHZOjqyr88QTVVF0/8nI8Lw+XH6+9M4p5YFP6lo5OByWFSv2cSzd06L1hkM2kiuXdCDx56MwcqQMQf70U3j8cRmu7iVrLatXH2D69A08/PAGfvhhP2VMPaozdCigUkr50+HDnm/UcnNlqOCzz8p8K5BW8507JXFFq1aF+7pYEqCUkuuUhIZKFrRhwypWfuUbHTpIWvVPP4XVqyX4veQSGRrqI65udEom7fF2jT9wrvP38MNw6BCprhoOcnOl1fuf/yx7MeuaokkTCXDdrcEVHi6JLJTyoCrrWnnW0/zxx4NMm/YL33yTisPhXWCTmQ233fYTa9YM9mr/ktLSchk+/DvWrz9MRoZcB556agvdujUiIeFcGjf2Mq17LVVLrmxKKVVHlJVSOiSkcA2QzEzprYiNlcV9L7hAhlOtXy/bW7f2fKyICEkzHRIijwYNZAjhW2/pmlQ1UUiIBFNz5sD//Z9PgyqQVvOdRbIN7tq1i1ZFA3e8X+MPnOv8DRpEUqtWNHc3kT4313M2y5omMBBuuEGGZLoSEABXXunfMqlapyrrmrfraa5YsY+BA1fw1Vf7vA6qCiQmHiIurmLJkcaOXc3atYdIT3ccX2YxPd3BunWHGT16dYWOWZtoYKWUUv4UGiqpb90txmiMzKnJz5cJ/h9+KJPkjx2TVvNNm2DsWNi4URY99RQg5ebKmPj//U96QX7+WQI1dzeJql7p168fycnJbNu2jZycHBYvXszIkSOL7VPuNf46d/b8mYyMLJ3aPC9Phgm6S95R3aZMkUaN8PDCnrbwcPldXn1VFmJVygOf1DUPrLVMmPDj8R6jipg69Wf27csq13u2b0/nq6/2kZ1deshhdnY+332Xyu+/H6twmWoDHQqolFL+9sADkgUwJaVwUdiCrEuzZ8uaJN98A7/+6noIUmampEpfuFDe4y4zYG6u3Pide27Z6/SoeicoKIh58+YxbNgwHA4HEydOpGvXrsfX95s8eTLvvfceL7zwAkFBQYSHh5e9xt+110qPqCthYdJjWvD+3Fx4/nl47bXCdXT695e13Tp0qOLfthICA6Wc69dLoopDh2T43xVXaFClvOKTuubB+vVHSElxkcmyHPLyLP37f0lgYAAdOzbg7rs7MmhQS4/vWbPmIMHBAWRluZ7LlZcnc71iYhpUqmw1ma5jpVQF6TpWyiNrJcPfxx9Lb9MZZ8gwvoLeoqws+OADuQk9dgx69ZL0uQXpd++4A5YscX/8wEDpterVS47lzimnwFdfVd3vVT66vk59tGABsZMmkdS6deH8vogI+WwvXChBvrWykOgPPxT//BojPUEffVSzgquaTetZPeXuPuT117dx/fVVe38SERHIpEntefrpXm73iY9P4brrEklLc79wdseODfj11wsJCKhpH9syeVVgHQqolFJVLSdHbhrHjZPFfN99V1JQn3UW/Pab7BMWBtdcA0uXwooV8MwzhUEVSLDliTFynmbNPO9XsOaIUv7yt79JUHTJJTJfsF8/+Ne/pBGhoOc0MVGGp5ZsFJAJGf5YvFqpOuudd3aWvVM5ZWQ4eOaZZDp0WMb77+9yuc+gQS3JzfUcV6ekZPLll/uqvHw1hQZWSilVUlqaLL57+eUy3Ofll+U1bz32mLTEZ2YWpp3OyJAhROPGeTd5/+yzPa9d1Ly5JKOYMMH9nKmICJg40ftyK1VVwsLg6afh888lZfPFFxefe/Xuu4XDYEuyVhbedZeJTynl1i+/HCEhYa/Pjr91azrXXbeGhx/eUGpbgwZB3H+/57Ts6ekOFizY4aviVTsNrJRSqqitW2Ul+aeflmQPP/0ETz0lC/Nu3172+7OypGXe3fC87GxJqV2WK6+U4X6uhIfDbbdJr9Xf/ibrWZUMwsLCJDX7zJkwZAj8+9+yKLFSNcGhQxJAuWOM5yGuSqlScnLyGTz4G49VqypkZOTz+OOb2bq19MiKKVPal7mawrFj7ocK1nYaWCmlVIGCeR+HDxdvTc/MlBvBSZPKPsbOnZ7X6ElPl4CtLA0bynyURo2k5wmkxT80VIYQjhkjr4WEwJtvSnrumBg48UT5Nz9fAsHt2+H332Vx4UGDJHBUqrrFxnrOTtmwIZxwgv/Ko1QdEB+fQmZmxTMBGiPrlXsjNzef11/fXur1pk1DiYpyP9oiMjKQIUNaVLCENZ8GVkopVWD9elmE11Vzn7Wwaxf88ovnY0RESPpod4KCvLth3LRJMvpFRsqQv3794OabZWjVgw8WZlYDCa7GjZNtP/4oQWBOTvFyZGfLcMZbbin73Er52tVXu2+ACA+XRozasoiwUjVEUtIhjh4tX29QYCC8805/rL2K/PyrePTRHoSFlV33HA7YubP0cF5jDA8+2IWICNcjLkJCArj22rblKmNtolctpZQqUFZvTkBA2fu0bg1tPXxpBAVBifVLSomPl7ldS5fCX3/Bvn0S0H3wQdlrUH3/vfshVNbCn3/C5s2ej6GUrzVpIvMYIyIKP9MBARJUDRkiPcdKqXJp3DiY4GDvsu0FBMCIES05cOBSrrqqzfHXb701hk6dym78CwyEHj0audx2883tmTKlA2FhAYSESKjRoEEQzZqF8NVXAznhhLq7QL0GVkopVaBp0+I9QSUZI/uUZfZs14knwsNlEr+nNNKHDsE//iHBUX6RtUCysiTAevBBz+f+88/CFNeuBAXBjro7cVjVImedBStXwj33wIgR0uu6eLHMB9TeKqXKbfToNgQGehdYhYUFsmBBfxo1Ci71+vffn094uJs5vk4BAYbx49u53GaM4YknevLbbxcye3ZX/vGPTrzySiwpKZfQq1djr8pXW+kCwUopVeCsszwPMA8OlgVMy3L66TB/viSO2LJFmvZCQmR40+TJnt/70Ufug7u8PEnNnpbmfmHSli3dJ70ACbpOOqns30Epf2jcWHunlKoiHTo04MYb2/Pqq9vIyHDdwBYaGkBgoOGDD86iSZMQl/tERgbxt7+dzOuv7yAnx/Viv/Pnn17s/cnJR3nzzT9JTc2mb98TufrqNrRtG8nf/+45S2Bdo4GVUkoVCA6GJ56QjHslh9OFhcGTT3o/szc2VhYHPnBAkl+cdJJ379261XM2tOBgmQfmLrAaONBza3/TptC9e9nlUEopVes8+2wvYmIieeSR30hLyyUvL5/27Rtw0klhGGM499xmTJnSgVatSo+qSEw8yD33rGPlygNYW7qNzxgIDg7gzTdP58orZfigtZY771xHXNxWHA5Lbq6lQYMg7r57PcuWncNZZ5Wx1mIdo4GVUkoVNXiwpEt/8klYs0ZeO/NMuOsu6N27/MfzZuhgUW3aSOY/d2v4ZGV5Xt8nJESGUt1yi+xXkIgjIECCw2ef9TzcUSmlVK1ljOGOOzpy222nsndvFuHhgTRu7Lpnqqj4+BTGjFlNVlZhD1XRPE6BgfDAA525777OxeZIvfTSNv77361kZxe+ryCd+oUXfsf27Re57RmrizSwUkqpknr3llTn1eGyy2TdLHfy82WNq+HD4fHHXfeCnX8+vP++BFE//CBB1eDBMHUqnHKK78qulFKqRggIMB7Tnh86lMObb/5JcvJRoqPDmTXr12JBVUnWQnJyerGgylrLffetLxZUFZWbm88rr2zlvvvqz3BADayUUqomad4cpk+HWbNcDwm0VnqiEhKkN8xdMovTToMXX/RtWZVSStU67767k/Hjf8QYyMhwEBIS4HYuVYH8fFi+fHex13755Qhpae7Tu2dl5fP116n1KrDStDtKKVXTjB0LCxZAr17u98nKkiGL6en+K5eq3bKyJAvft99KAhSlVL3zv/8dZvz4H8nMdBxPcFFWUFXgyJE8WrZcwl13rWP37kwWL95Z5nvKyi5Y12hgpZRSNVFsLAwd6jnDX1AQ/Pqr/8qkaidr4YUXoG9fyUp5662SuXL6dM+LWSul6pzHHtvsdSDlyr592fznP7/TvftnbNlytMz9L7ooqsLnqo10KKBSStVUwcESWLlblyo/X/ZRypMXXoB58yQ7ZVHvvAMZGTJXTylVL6xcuR+Hw5a9owc5OZZDh3JYv/4wYWEBbudmGQOjR0dX6ly1jfZYKaVUTTV4sOcMfiEh0K2b/8qjap+sLNdBVcG2jz+G3btLb1NK1UlVNTQvPx/+/DMD4+Y7KiAAJkxoS4MG9avxTwMrpZSqqdq1g2HDJE16SeHhcP/9nocKKrV2rcfPSEJGBp369iUmJoa5c+eW2m6t5fbbbycmJoYePXrw008/+bK0StVZCQkJdOrUqdrr2vXXn+JVcGWMLCbsSVhYIP/6V3ciIgIJDi4MsCIiAujRozHPPtun0uWtbTSwUkqpmuzJJyWZRVgYnHACREZC48YwcyZcdVV1l07VdHl5bns9HdYydc8elt9zD5s2bWLRokVs2rSp2D7Lly8nOTmZ5ORk4uLimDJlij9KrVSd4nA4mDp1KsuXL6/2ujZpUnuaNw8hKMjzeobWgsNhPa43n52dz7hxbdmwYRhTp8bQt++JnH9+c15+uR9r1gyiQYP6N+Oo/v3GSilVmwQFwcMPw913S6KKkBDo2tX1+lVKldS7N+TkuNyUmJ1NTEgI7UeNgpAQxowZQ3x8PKeddtrxfeLj47nuuuswxtC/f38OHz7M7t27iYqqXxPSlaqMxMREYmJiaN++PUC11rVGjYJJTBzMzTevJSFhDwEBkJnpeo5UXp77uVjBwYZhw06iSZMQmjQJ4emnPWSxrUe0x0oppWqDBg2gXz/o2VODKuXW2rWHuO22nxgzZhVPPbWFQ44wGD3a5XDSFGNoc9JJEBMDQHR0NCkpKcX3SUmhTZs2x5+72kcp5Zk39cifda1lyzA++uhs/vrrEm65JabYML6SIiICCQ0NKDaiODw8kKiocOLi+vqkfLWZsdZjZpDKpQ1Rqg6LjY0lKSmpuouhVE3meayJn3Xt2tWGh4dXdzHKJTU1lebNm3u177Zt6Rw+nEt+vnx1BwTInz+mQyQnpO2Fw4dlWKC1YAyHgoJICw2lbbt2ABw4cID09HROPvnk48dMTk4mKiqKBg0aALBlyxaio6OJiIhwWdb9+/cDkJ2dTS9P67DVQOX5W9cka9eu3WitrTFZbGpjPQPf/v8fOnSItLQ02rZtC1SurlV1PUtNzWbXrszj142SAgIM7dpFcORILkeP5hEQYGjWLIRmzUIJDKzYJb421jVv65k2eyqllKoXwsPDa11jiLcNOE89tZmHHtpIfn5hav585+ielL8C2bbtIprn7IdvvpF5V2eeyar9+5kxYwaffvopAI8++igA999///Fj3HzzzQwcOJCxY8cC0KlTJ7788ssyhydFRkbW2b91TWOMyaruMhRVG+sZ+Pb/f9WqVT6pa1VRz/bty6Jt20/cpkxv2TKM5OSLjzfUVIXaWNe8rWc6FFAppZSqxay1PPbYZjIyXK93lp8Pr7yyFVq3hmuugeuug1NPpV+/fiQnJ7Nt2zZycnJYvHgxI0eOLPbekSNHMn/+fKy1rF69mkaNGun8KqXKqSbXtRYtwvj73zsREVE6U2B4eCAvvti3SoOquk57rJRSyt/S0uC112DxYjh6FNq3h1tukdTqntatUsqFtLQ8Dh50naACIDPTwcqVB0q9HhQUxLx58xg2bBgOh4OJEyfStWtXXnzxRQAmT57MiBEjWLZsGTExMURERPDaa6/57PdQqq6q6XVtxoyuREdHMH36xuPDiU89tQFPPtmToUNP8ls56gINrJRSyp+OHIFRo2DPHsjOltd++UWy/o0dCw89VL3lq8MmTZpU3UUoN2/KHBbmefCJMXDiiSEut40YMYIRI0YUe23y5MlF3mt4/vnnvShpcc2aNSv3e6pbbfx8OMVVdwGKqq1/R1+X2xd1rarqmTGGm25qzw03nMLu3VmEhATQvHlolRzblVr6GfGqnmnyCqUqqDaOEVY1wPTpsGgR5OaW3hYeDm+/Dd27+79cvlHTut/q7HfaxRd/z7Jlu3H1lR4ZGcjSpecwcGALv5VHr49+pfWsntJ65lde1TOdY6WUUv5iLbz7ruugCqQHa8EC/5ZJ1QlPPtmTE04IKjWSNDw8kAEDmnPeebUrA5dSStVGGlgppZS/ZGcXDv9zJT8fdu3yX3nqofvuu4/OnTvTo0cPLrvsMg4fPlzdRSrTu+++S9euXQkICHDbOt2p0wmsXj2IIUNaEhxsCA8PpFGjYO65pyPx8Wdj/DR3LyEhgU6dOrFhwwbmzp3rl3NW1sSJE2nRogXdutWYjOVl2rlzJ+effz5dunTBGLPRGHNHdZepqLpaz2qShIQENmzYQExMjNY1Hyqoa8aYX72paxpYKaWUv4SGykK/7gQGHl+sVfnGkCFD2LBhA//73//o2LHj8bTHNVm3bt344IMPGDBggMf9unRpyKefDuDAgVH88cdw9u8fyezZ3QgOdv9Vb61l2bLdXHDBCk4+eSn9+3/JokV/4nCUfzSXw+Fg6tSpLF++nK5du7Jo0SI2bdpU7uP424QJE0hISKjuYpRLUFAQTz75JL/++itAf2CqMea0ai7WcXW5ntUEBXXt1FNPZdOmTVrXfKigrllru+BFXdPASiml/CQj08ELHabQe/edxOz6O9emjmFddpF0ukFBkgpb+czQoUMJCpK8Tf3792dXLegh7NKlC506dfJ6/xNOCCYqKpygIM9f8dZapk79mdGjV/H116ns3JnJmjUHuemmJC6+4aSzEgAAIABJREFU+Dvy8lyva+NOYmIiMTExtG/fHmMMY8aMIT4+vlzHqA4DBgygSZMm1V2McomKiqJPnz4AWGuPAr8Crau1UEXUh3pWnQrqWmhoKCEhIVrXfKi8dU0DK6WU8oPDh3Po2/cL7v20BeuyW/FHXjMWpffkrD1TeeNYX0lccc892mPlR6+++irDhw+v7mJUmy++2Mf8+dtJTy++/lV6uoNvv93Pyy9vK9fxUlJSaNOmzfHn0dHRpKSkVElZlXvGmHZAb2BN9ZbEtfpez3xB61r18Kauabp1pZTyg3vuWc/WrcfIySkcYpVPIJk2kMmHrmTomx2IGtqvGktYdxhjvgBKLb7y0UcfMWrUKADmzJlDUFAQ48aN83fxXBo8eDB79uwp9fqcOXOOl7mqPf30llJBVYGMDAdPPbWFyZM7eH08V1mG/TW3q746duwYwPvAndbaNH+eW+tZ9dG65n/GmAZ4Udc0sFJKKR/LzHSwaNHOYkFVMcHBvJIYyYND/VuuuspaO9jdJoA33niDpUuX8uWXX9aYm5EvvvjC7+f8/fdjHrenpGSW63jR0dHs3Lnz+PNdu3bRqlWrCpVNlS03N5crrrgC4E1r7Qf+Pr/Ws+qjdc2/ciWT7/t4Udd0KKBSSvnY/v3ZpdJgF5WVlc+vv/q1sbneSkhI4F//+hdLliwhIiKiuotTrU4+2fPv37Jl+RYI7devH8nJyWzbtg1rLYsXL2bkyJGVKaJyw1rLDTfcQJcuXbDWPlXd5SlJ65lvFdS17OxscnJytK75UEFdA371pq5pYKWUUj7WpEmIxyxrISGGdu0i/Vii+uvWW2/l6NGjDBkyhF69ejF58uTqLlKZPvzwQ6Kjo1m1ahUXXXQRw4YNq5Lj3nnnqURGBrrcFhERyO23n1qu4wUFBTFv3jyGDRvGhg0bGD16NF27dq2KovrU2LFjOfPMM9m8eTPR0dG88sor1V2kMq1cuZIFCxbw1VdfYYxZ53yMqO5yFdB65lsFdS05OZkuXbpoXfOhgroGXOBNXTOuxmkWoatnK+WGrniuyuPKK38gPv4v8vJKX1bDwgLYuHEY7dt7SMVeO9WM8T+F9DutCGst48atYcmSv4rNtYqICKRXr8Z89dV5hIa6DrzKotfHqmOtZdWqA3z22V4CAw0XXRRFnz4nFt1F61k9pfXMr7yqZzrHSiml/OC553rzww8HOHgwh+zswjTWERGBPPhgl7oYVKkazhjDwoVnsHjxTh5/fDM7dqTTokUYt90Ww003tSckRAe1VLdDh3IYNuxbNm1KIyPDQUAAzJ37G/37N2XJkrOJjNTbOKVqEu2xUqqCtKVIldf+/dk8+eQWXn99OxkZeXTv3ojzz2/BqlUHSE3NJjb2RO66qyPdujWq7qJWFW1Jd+PgwRwWLtzB778f45RTIrn22rY0b16+OU2+9Mcfx3jvvV0cPZpL//5NGT48isBA7/879fpYNQYOXMGqVQfIySm+plhYWAAXXxzFu++eBVrP6i2tZ37lVT3TwEqpCtILmqqMnJx8hg79lrVrD3HsWB4AgYGGkJAA/v3vXtx0U/tqLmGV0Bs+F956609uvFGuHZmZDsLDA7AWnn++DxMnnlItZXI4LMuW7eannw6RkLCHn38+RH4+5OZawsMDMMYwYEAzrr76ZK6+ug3h4Z6HCOr1sfI2bUojNvYLMjNdp8QPDQ1g69YRtGoVrvWsntJ65lde1TPt51dKqWrwyCO/kph44HhQBXJzm5np4I471pWZClvVTuvWHeamm5LIzHQcv2HOzMwnKyuf2277mVWrDvi9TJs2pXHyyUsZN24NM2ZsYvXqg2RnW3Jz7fHyZWQ4SEjYy223/Uy7dp+QnHzU7+Wsb374Yb/HbKKhoQEkJh70X4GUUmXSwEoppfzMWsuzzyaTmZnvcnteXj7/+c/vfi6V8od//es3srJc90BkZjp45JFf/VqejIw8zjvva3bvzuLo0bwy9z92LI/U1GyGDfvO5SKlquqEhQUSEOC5kTwsrGLJRZRSvqGBlaoX2rVrR/fu3enVqxexsbGltltruf3224mJiaFHjx789NNP1VBKVV9kZjpIS3N/E5uba/nllyN+LJHyl++/30++63gaa2HZst20aLGECy5YwWef7fFpWQ4ePEi/fgM5cGAy1s4G3PWSTgXuAe4DpmEtpKZm8/XXqT4tX303fPhJ5OW5+bAA+flw3nnN/VgiVREHDx5kyJAhnHrqqQwZMoRDhw653K+s+xRVO2hgpeqNr7/+mnXr1rkcj7x8+XKSk5NJTk4mLi6OKVOmVEMJVX0RFhZIcLD7luiAAIiODvdjiZS/RER47mHIzy8MWi677AdmzNjos7LMnTuXoKDuWPss0B34yMPe04HHgbmAzBFct+6wz8qmoGnTUO66q6PLz0xERCCzZnUtc66bqn5z585l0KBBJCcnM2jQIObOnet2X0/3Kap20MBKKSA+Pp7rrrsOYwz9+/fn8OHD7N69u7qLpeqogADDtde2dRtchYUFcvPNHfxWnry8fNLScsnP16FdvjZhQjvCwrz76s3IcPDYY5vZuLHs3svsbAfvvruTJ57YzDvv7HQ73LCo+Ph4unQZ6Xx2HvCjV+UCCA42NG4c7PX+qmLmzOnGww+fRqNGwZxwQhANGgTRtGkITz7Zk7vu6ljdxVNeiI+PZ/z48QCMHz+ejz7y1IChajsNrFS9YIxh6NCh9O3bl7i4uFLbU1JSaNOmzfHn0dHRpKSk+LOIqp6ZM6cbLVuGlVorKDIykGuuOZn+/Zv6vAx//ZXJtdeuoUGDD2nWLJ5mzeKZPn1DqdTOqupMmdKBZs1CCQryLpFbTo6D//znD4/7fP75Xlq2XMINNyTxwAO/cOONSbRosYRlyzw3Du3du5cbbuhDgwZBwIlAmoe95wD/AL4AJNHKpZe2LrZHXFwcsbGxxMbGkpqqwwSrgjGGf/yjM/v2jWTFioF8//357N07ksmT/dfwoipn7969REVFARAVFcW+fftc7lfWfUoBrWc1m64sp+qFlStX0qpVK/bt28eQIUPo3LkzAwYMOL7d1SRs4yIdU1xc3PELnl7QlCfbtqWzbVs6rVqF0blzw1LbW7QI4+efhzB37m+8+uo2jh3LIyoqnJtvbs+0aZ18Xr49e7Lo3ftzDh7MIS9PPv+HDuXy+OOb+f77/Xz22XnlWrdIeadx4xC+/XYg11//I6tWHSQ0NMBj0giHA5KT3WeI/O23NEaNWlksJXdurhzvqqtW0b37sxw7tr/U++bMmQPAoEEt6NatIevWHSYry91ZZgNNgCPAPwkNjWbGjKto0iSk2F6TJk1i0qRJADpHpIqFhATQp8+J1V0M5cbgwYPZs6f0nMiCeuaNsu5TCmg9q9k0sFL1QqtWrQBo0aIFl112GYmJicUuWNHR0ezcufP48127dh1/T1F6QVNl2b49nWuuWcPPPx8iNDSA3FzLKadE8uabZ9CzZ+Ni+zZrFsrMmV35449jLF36F/v2ZTFjxgaeeWYLr7zSj0suKf0ZrCozZvw/e/cdH1WV93H8c2YmHYi0IBB6qKFECEWkiXR8QPRRsbuoiG11dVWsq+5aV11de9ayKs/CKhaUpSgouMuCISjuQkQBpQUCQUHSy8x9/rgBApmZ9Jkk832/XvMiM/fOvb+ZcCb3N+ec39l8QlJ1VH6+h9TUn/nHP/YxbVrdnT8Uud0W9923ieee24Yx4HTac2UKC90UFXkfhulyGXr2bOrzmA8+mO5znaP8fDdduz7G3/42zOv2Nm3asH9/JitWjOaKK5bz/vuxNGsWRkmJhzZtIrn33t4UFXl45ZUf2LTpF4xpTnT0cCZPLuDOO3tV/Q0QaaRWrFjhc1ubNm3Yt28fbdu2Zd++fcTFxXndr6LrFGkYNBRQGr3c3Fyys7OP/fzJJ5/Qt2/fE/aZNm0ab731FpZlsW7dOmJjY4913YtU1qFDRQwdupIvv/yJggIPv/xSQl6em82bjzBy5Of88MOJPQ+Zmfm0a/cxH364l5ISKCjwUFwMWVlFTJu2hnvu+W+dxTpv3s5ySdVROTluXnnF//Azqbpf/Wo9zz67lZycErKzS8jP97B/fyEeD7h8fM0ZFubg+ut9D/v6+OO9PrdZFixd6ruy4LRp03jzzTeJiXGRnPxfbrxxJkuXjiQ1dRzbtk3mV7/qwqWXnsrq1cM4eHA627aNpXfvHVx++chKv2aRUHe0nQG8+eabTJ8+vdw+lblOkYZBiZU0evv372fEiBEMGDCAIUOGMHXqVCZNmsTLL7/Myy+/DMCUKVPo2rUrCQkJXHPNNbz44otBjloaopSU7WRnF3stp52f7+bRR7ccu19Y6GbgwBV+y64/+ugWPv/c+3j8mvLVy3FUVlZhnZw3VG3dms277+4mL6/8+24nuIaoqBP/JEdHO7nvvt707l1+KOlR3o5Xlr8iFnPnzuXTTz+le/fufPrppzzwwD2cfnpLTjklh6lTpwLHPz9Hjkxm0qQRxz4/pX7Izi4OdghSgZPb2dy5cwHYu3cvU6ZMAXxfp0jDYypY4E8lokR8SE5OVklUOUG/fsvZtMl3AYDYWBeHD88A4P/+bydXXrneZ6/RUUOGtODLL8+q1TgBEhKWsH17rtdtYWGG2bO78vzzA2t6mvo2SStof9Mef3wL9923ieJi7yFERzv5zW+68+GHezl0qIj+/U9h7txeftcpsiwLh2Oh3/OeemoE+/ZN87tPXdDnY90qLvbw299+w1/+8iN5eeeqnYUotbOAqlQ70xwrEZFaUlE1vV9+KeGzzw4wdmwc8+fvrjCpAtiwwftikjU1d24vbrllI7m55Xs0XC4HN9/cvU7OG6ry8kp8JlVgz6U655z2/OEP/Sp9TGMM7dpFsnevz6oTTJx4apXiDDTLsvjyy59JSfmBvXvzGTiwOXPmdKNjx+hgh1avXXFFKosW7a2w51lEAktDAUVEasmkSaf6XfgX4Oyz/8n48av56qvKJUyOOvqUvuqqLlx4YQeio53HzhEe7iAqyslf/jKI7t19F0yQqjv1VP8LPhcXe/wO+fPl7rt7+1wXKyLCwd13967yMQPF47G44or1nHXWat58cwfLl+/n6ae/p1evZcyfvyvY4dVbW7dm88EHGRUOAxWRwFNiJSJSS37zmx64XP4/VvPzPaxYcYB9+3z3MpQ1YULd9DgYY3jttcGsWjWGX/2qC5Mmncqtt/Zgy5ZJXHJJpzo5Zyh77709frdPmdKWmJiqDyK57rpuTJ/enpgY57HHXC6IinLy8suD6NGj/ibIKSk/8N57e8jLcx+bl1hY6CE/381VV6WVK/Yito8/3ud1HqeIBJ+GAoqI1JLOnWO4/faePPRQeq0cLzraye9/n1grx/Jl8OAWDB7cok7PEery892sXu1/3bvqJFUADodh/vyhrF6dxcsv/8C+ffZwuhtuSCAhoUm1jhkojz22xWevS0mJh+ee28af/pQU4Kjqv6IiD263MiuR+kiJlYhILfn55yL+9red1XquMXZ5bIDwcENcXCRvvz2E007ToqANXX6+G4fD4G9O/5Ej1a/uZoxhzJg4xozxvj5OfeTxWOzcmedze3GxRWrqzwGMqOEYObIVkZFOr/MjRSS4lFiJiNSSiy9ex65d+dV67hNP9MfhMHTsGE3HjtEMHtwcY+pbsS+pjubNw2jRIozMTO8l7KOjnYwZ47v6X2PkcBiiopx+iy+0bh0RwIgajuHDW9K9exM2bz7ityCKiASe5liJiNSCnTtzWb06q8LKgL7cdFMCt97ag//933iGDGkR0KQqM7OAW2/dSJs2HxEb+wHjxq2ucOiaVJ4xhrlzexEd7fS63ek0XHFF58AGVQ9cfHFHXC7v/89jYpzMnt01wBE1DMYYli8fRWJi7Alz60Qk+NRjJSJSCzZuPEx4uIOCguolVmFh/r/n2r49h8WL91FS4uHMM+MYOLB2hgj++GMugwev4MiR4mPffq9ceYC1a3/iqacGMGdOt1o5T6j79a+7s3nzEebN20VxsYeSEouYGCcul2Hp0lGccko4RUUePvggg5Ur9xMZ6eSCCzpwxhktG23P5e9/n8hHH+3l558LcZfpuIqKcjJ8eCsmTfJduKW42MOnn+7nwIFCevVqytChgf0yItji4iL56qtxrF37U7BDEZEylFiJiNSCZs3CKCqq3pyHIUOal87BKa+42MPll6fy4YcZgD03xeVykJR0CosXj6B58/BqxwxwzTVpHDpUVK7KWF6em9/8ZiMzZrSnTZvIGp1D7F6GlJRkbrmlB2+/vYOffipi6NCWzJzZgZgYF9u35zBq1OdkZ5eQnV2CMfD66z+SnNyCpUtHEhXV+Hom2raN4uuvx3Pnnf9h4cI9eDwWTZuGccst3Zk7t5fPNvHRR3u58spU3G6r9P+tRdu2UXz00Rn06lX1kvUNlTGG4cNbBTsMESnDWJbf8bkavCvig1Y8l7IOHSqkRYuPqvy8yEgHn346mhEjvF8gXXfdBt58c2e5uSjh4Q6GDm3BF1+cWa14AbKyCunQYTGFhd572SIjHTz8cF9uvbVndU9R37oQ6vRv2rp1P/G7321m9eosHA7DxIltePDBRPr3P8Xv8zwei65dl7B7d165BDcy0sFFF3Xk9dcH12Hkta+qn48lJXaZ9SZNXH57ntat+4mxY1eXaw/GQIsW4WzdOrnGXzY0QCHVzuQ4XYcEVKXameZYiYjUgg8/3EtEhO+PVJfL0LNnU6KiHDRp4qRJEyfNm4exYMEwn0nVoUNF/PWvO7xO8C8q8rBhwyG++eZwtWPev7+A8HDfMRcUePxWbpPjPvwwg7POWs0nn+w/thbTokV7Of30zyqcr/bpp/v56afyvYZg/w7mz9/F4cNFdRR5/eByOWjaNKzC4Xz33rvJa3uwLLv64muv/VhXIYqIVEiJlYhILcjIyPdbuCImxsWrrybz73+fRUpKMu+9N5z9+6cxfXp7n89JSztERITvIWBut1WjIhPt20f5jTk62lmvF5itLwoL3VxxRWq5NZksyx5SefHF6/B4fH+Jn5Z2iLy8Ep/bIyKcpKcfqbV4G5olS/YxZMgKIiPfY+XKAz73y8tzs3Ch/4WYRUTqkuZYiYjUgi5dYoiJcZGT4/0CubDQTadO0XToEE1Skv+hYUeFhzvwN1zb6TR+e5wq0rx5OJMnn8o//rHPa9lmy4JLLulY7eOHimXLMvE3qj47u4R1637yOR+maVOX38InbreHJk1C88/1H/+4hQceSPe5kPDJQqh+hYjUQ+qxEhGpBTNm+O55cjoNQ4a0oEOH6Codc9iwFn4v2D0ei7PPblulY57slVcG0a5d1AnFEZxOu7dq3rwhnHJKyM1XqbLMzALcbt+/KGNg/37va1gBnHtue7+/5+bNw+nXL7YmITZIe/fmc//9myudVEVHO5k5s0MdRyUi4psSKxGRWhAd7WLhwtOPldAuy+22yMoq5P33qzZMKSLCye9/39fr+kdhYfYCqxMn/pM77viG3burNxcqLi6S//53Ao8+2pd+/WLp1i2Gyy/vzPr14zj33PhqHTPU9OzZ1GcFO4CSEovu3Zv43B4fH81113Xz+nuOinLy8suDQqqU+FHz5u30m3CW5XDYa19deWWXug1KRMQPJVYiIrXgwIECtm3L4aqrupQu8Hvi9m+/zebyy1N56KH0Kh335pu78+ij/YiNDaNZMxdNm9pDwjwei0OHiklPP8Kzz26jT5/l1V7TpmnTMG6+uQf/+c8Etm2bwuuvD6ZPn9ApW11To0a1pnnzMK/bHA7o0aMpffv673F6+ukBPPxwX1q1Cic62klEhIM+fZqxaNEZTJlSs15JX959910SExNxOBx+K4stW7aMnj17kpCQwGOPPVYnsXizZ0++z4qVR0VHO4mOdtK7dzP+/e+ziI31/nsQCZb63s6kdimxEhGpoUce+ZZOnf7B7bd/w3PPbWPdup+8ftOem+vm0Ue/ZdeuqvUu/frX3TlwYBpLl46kffsojOGEBVWLijzk5JQwbdq/KCmp3gLFUn0Oh+Hjj0cQGxtGZOTxP6vR0U5atozgvfdOr/AYxhhuuaUHmZnTSE+fyI4dU9m8eSLjx7eps7j79u3L+++/z6hRo3zu43a7ueGGG1i6dCnp6enMnz+f9PSqfTlQXYmJzYiJ8V28JSzM8NxzSfzrX2eyadNEEhJ89wqKBEt9b2dSu5RYiYjUwDvv7Obhh7+loMBDfr4Hy8Jr2eyjPB54660dVT5PeLiD1q0j2LEj1+fwqMJCD8uWZVb52FJzAwacwtatk7nnnt4MHHgKgwc356GHEvn++0l07Vr5C36n09CpUwynnlr3izL37t2bnj39r1GWmppKQkICXbt2JTw8nJkzZ7Jo0aI6jw3goot8F04JD3dw3nnxzJrVldNOax6QeESqo763M6ldSqxERGrgvvs2VXpyPdi9SxkZ+dU617x5O31WjgM7sdq+Pbdax5aaa906gnvv7cOGDeNJTR3Hbbf1bPDFPzIyMujQ4XhBiPj4eDIyMgJy7mbNwnj33dOJjnYSFnZ8bG1MjJPOnaN58cWBAYlDpK4Fs51J7QrN+q0iIrWgoMBd5UQmJsZZrQpve/bk8cQT3/ndJyLCEZCeDmk4xo0bR2Zm+V7Mhx9+mOnTp1f4fG/l/n0V0khJSSElJQWArKzqr69W1uTJbfnvfyfw5z9v5fPPs2jSxMVVV3Xhoos6nlDJUiSYGno7k9qjxEpEpJqczupVarv00k5Vfs5LL22vsEKaZcG0ae2qFZM0TitWrKjR8+Pj49m9e/ex+3v27KFdO+//x2bPns3s2bMBSE5OrtF5y+ratQnPPHNarR1PpLY1hnYmtUNDAUVEqikszMGYMa0rtW94uIPoaCcLFw6nWbOqVy5bt+7nCiukvfLKQH2LL7Vq8ODBbN26lR9//JGioiIWLFjAtGnTgh2WSKOidtZ4KLESEamBJ57o73Odqfj4KEaPbsWgQc359a8TSE+fyKRJp1brPK1bR/jd7nIZZs70Pdlf5GQffPAB8fHxrF27lqlTpzJx4kQA9u7dy5QpUwBwuVw8//zzTJw4kd69e3PBBReQmJgYzLBFGhS1s9BivI3rLKOSS/OJhJ7k5GS/a1JI6Fiz5iCzZq1nz558wsIcFBa6mTq1La++mlxrxQtWrtzP9OlryM0tXyjD6YQLL+zI//3f0Fo5Vy2qb6va6m9agOjzMaDUzkKU2llAVaqdaY6ViEgNnXFGK777bjJbthzh55+L6NGjKa1a+e9hqqqxY+MYMyaOzz8/cEIVQocDYmPDeeSRvrV6PhEREakaDQUUEaklvXo1Y/jwVrWeVIFdIerDD4dz9929adkynPBwB+HhDs49N54NG8bRqVNMrZ9TREREKk89ViIiDYTL5eCee3pz1129yM4uISrKSXi4vh8TERGpD5RYiYg0MA6HITa26pUFRUREpO7oq04REREREZEaUmIlIiIiIiJSQ0qsREREREREakiJlYiIiIiISA0psRIREREREakhJVYiIiIiIiI1pMRKRERERESkhpRYiYiIiIiI1JASKxERERERkRpSYiUiIiJSB774IoupU/9J+/Yf07fvcl54YRv5+e5ghyUidUSJlYiIiAREVlYhv/nNRlq0+JDw8IUkJi7j//5vJ5ZlBTu0WvfYY1uYPPmfLFmSyd69BWzefIQ77vgPQ4asIDu7ONjhiUgdUGIlIiIitSo/382rr/7A3/++myNH7CRi//4CkpI+4cUXt3PoUDHFxRbp6dlce+0Gbrrp6yBHXLu+/fYIDz2UTl7eib1TeXlutm7N4f77NwcpMhGpS0qsREREQtC7775LYmIiDoeDtLQ0n/t17tyZfv36kZSURHJyst9jZmYWMGTICrZsyebmmzdyzTVpnHrqxzz55Hfceed/OHCgkKIizwnPyc1188YbO9iw4VCtvK764MUXt1Nc7PG6rbDQw6uv/ojH0/h66aS8umhnUn+5gh2AiIjUvezsYt58cwd/+9tuSko8TJvWjmuv7Ubr1hHBDk2CpG/fvrz//vtce+21Fe77+eef06pVK7/7uN0Wo0d/zg8/5OLxWCf01tx//yaKiy1KSrwnEwUFblJStvPKK43jgvL777N9vlawX29ubglNm4YFMCoJhtpuZ1K/KbESEanHPvvsAA89lM769T8TEeHg/PPjueeePnTsGF3pY+zencfQoSv55ZfiYxe7//3vLzz55Pd8/vloTjuteV2FL/VY7969a/V4S5bsY9++Aq8JRX6+996bozwe2LUrv1bjCaaEhCasXLkft486FRERDmJidAkWCmq7nUn9pqGAIiL1UE5OCVdckcqkSV+wenUWeXluDh0q5vXXdzBgwCd89112pY914YXrOHCg8IQehIICD7/8UszZZ/9LQ5LEL2MMEyZMYNCgQaSkpPjcb8mSfWRnl1TrHOHhDgYMiK1uiPXO2We3xRjjdVt4uINf/aozDof37RKaKtvOpH7T1yUiIvXMxx/vZebMteTllf+Wv6TE4pdfirnqqvX8619jKzzWtm05bNx4CLfbe/KUnV3CihX7mTDh1BrHLfXPuHHjyMzMLPf4ww8/zPTp0yt1jDVr1tCuXTsOHDjA+PHj6dWrF6NGjSq337fffgDML713pNx2pxPCwhwUFJT/f+10GubM6VapeOozj8fiuuu+4q23dnj9wiIqykGHDtH84Q/9ghCd1JVAtrOUlJRjiVdWVlbNApdap8RKRKQeSU8/wsyZ67wmVUdZFmzYcIiMjHzat4/ye7zvv88mPNzpcyhWUZGHLVuylVg1UitWrKjxMdq1awdAXFwcM2bMIDU11esF3z333MSGDYPIySkB5pbb3r59FB06RLNx42Fyc+3eU6cTwsMbBs0mAAAgAElEQVSdvPDCaXTuHFPjWIPtiSe2MG/eTh/JI9x/fx9uvLE7TZro8qsxCWQ7mz17NrNnzwZQkYt6SEMBRUTqkccf30Jhof/5KGAPJzpwoKDC/eLiIigp8X288HAHbdpEVilGCR25ublkZ2cf+/mTTz6hb9++Xvc966w4EhKaEB5e/tIiOtrJk08OYNWqMbzxxmDGjm1N//6xXHllZ9avP4tf/apLnb6OQCgp8fDEE9+VK7F+VFSUi86dY5RUSTlVaWdSvymxEhGpRz7//IDPYXtlFRV56NCh4gIWgwY1p3nzcJ/bPR6L//mftlWKURqHDz74gPj4eNauXcvUqVOZOHEiAHv37mXKlCkA7N+/nxEjRjBgwACGDBnC1KlTmTRpktfjORyGzz4bzZlntsYYaNbMdez23HOncf75HXC5HJx/fgdWrhzDN99M4NVXB5OY2DjmVu3alVeulHxZOTklfPbZgQBGJPVBbbczqd9MBauda0aziA/Jycl+16QQqY4ePZaydWuO333CwgyTJ7dl0aIzKjzegQMF9O27nKysonLbXC7DW28N4aKLOlY73grUt9n5+psWIP37D+SBBz6iadMwRo9u7bUXq7HZty+fLl2W+OxxNgZuvrk7f/pTUm2fWu0sROk6JKAq1c4a/yediEgDctllnYiM9P3R7HIZ2reP4i9/GVSp49111385fLjY6zan09C9e5NqxSkNj2VZfPzxXs48cxWdOv2DESM+Y+HCPXVSFTI83MG558YzfnybkEiqANq2jfLbnqKjnXX5JYaI1AOh8WknIW337t2ceeaZ9O7dm8TERJ599tly+6xatYrY2FiSkpJISkrioYceCkKkInD99d1o1iwMp7P8l2NOJzz4YB/++9+JxMVVPC/K7baYP38XxcXeL5yLiz28/PIPNY5Z6j/LsrjqqjQuumgdq1ZlsWtXHmvW/MSVV6Zy/vlrVXK/lvz5z6cRFeUs93hUlJORI1sxeLDWjBNpzJRYSaPncrl46qmn+Pbbb1m3bh0vvPAC6enp5fYbOXIkGzduZOPGjdx///1BiFQEWraMYP36cYwc2YrISAexsWFERjoYPrwlW7ZM5u67+1R68nteXonPpArsRVl3786rrdClHvv44328887uY9X4jsrNdbN8eSbz5u0MUmSNy5lnxrFo0Rl06xZDdLSTZs1cREU5ueqqznz44Rk+17YSkcZBpWmk0Wvbti1t29qT85s2bUrv3r3JyMigT58+QY5MQtmOHbk899w2vvgii1NOCeOqq7pw3nnxhIU56Ngxms8/H0NGRj67d+cdK1NdVTExLpo0cfkcChgWZujfv3EUDhD/nn76u3JJ1VG5uW6eeup7Lr+8c2CDaqTGj2/D1q2T2bYth5ycErp3b6pKgCIhQi1dQsqOHTv4+uuvGTp0aLlta9euZcCAAbRr144nn3ySxMTEcvtoYT6pDf/4xz4uuGAtJSUeiorsHqW1a3/iiSe+44svzjx2Eda+fVSF61T543AYbrghgaef/s7rOlYul4Prrmv4i7JKxX74IdfvdvVc1i5jDN27Nw12GCISYBoKKCEjJyeH8847j2eeeYZmzZqdsG3gwIHs3LmTb775hptuuolzzjnH6zFmz55NWloaaWlptG7dOhBhSyNz6FARF1ywlrw897GkCuxeg/T0I9x22ze1er577+3NwIHNiYk5Pu/D6TRERTl59tkkunZV8YpQUFGCfuqpWstMRKSmlFhJSCguLua8887jkksu4dxzzy23vVmzZjRpYl9gTpkyheLiYg4ePBjoMCUEvPXWDp/bCgs9vP32TvLzvQ/Zqo7ISCerVo3h9dcHM2ZMa/r2bcZll3Vk3bqxXHNN11o7j9Rvt9zS44TkuqzoaCc339w9wBHVnsJCN59/foClS/dx8GBhsMMRkRCmoYDS6NnVsK6id+/e3HrrrV73yczMpE2bNhhjSE1NxePx0LJlywBHKqHgm29+IS/Pd+LkcEBmZgFdusTU2jldLgcXXNCBCy7oUGvHlIbl/PPj+dvfdrFy5f4T5lpFRzsZOrQFs2Z1CWJ01ffii9u4667/AvY6UYWFHi64oAOvvDKIyEjviaSISF1RYiWN3po1a3j77bfp168fSUn2woyPPPIIu3btAmDOnDksXLiQl156CZfLRVRUFAsWLFD1JqkT7dtHERZmfFbrKymxaN48LMBRSWPncBjef384b7+9k6ee+o7du/Np2zaSW27pzqxZXQgLa3gDWP7ylx+4/fb/lPui4p13dvPTT4UsXjwySJGJSKgyluV37QotbCHig1Y8l+rYvj2Hvn2XU1BQvpiEwwFnndWGTz4ZVeFx9u8v4MUXt7F48T5cLsMll3Tiyis706xZvUrK6tu3E/qbFiB1/fnodlu0afMRP/1U5HV7VJSTL788i379QqLqpdpZiNJ1SEBVqp01vK+oREQasG7dmnDrreXnuzidEBsbxosvDqzwGBs2HKJHj6U88cR3fPXVYVJTD3HXXf+lV69l7Nql6m7S+P3nP4cpKir/5cRRxcUeFi/eG8CIRESUWImIBNzDD/fj9dcHk5jYDKfTEB3t5LLLOvH11+NJSPBfpc/ttjj77H9x5EjJCb1eeXluDhwoZObMdXUdvkjQud0W/kZrezwWJSXqOBGRwNIcKxGRIKhuMYlPPskkN7fE6za322LjxkNs25ZTYYIm0pD16xeLv5kMUVFOxo6NC1xAIiKox0pEpEH5/vscv0OgwsMdbNuWE8CIRAIvIsLJbbf1IDq6fOW/sDBDz55NGT5clV1FJLDUYyUi0oC0aRNBeLiDwkLvyVVJiUVcXESAoxIJvPvu68OhQ8W88sp2nE6D223hcBj6949l8eIRquwqIgGnxEpEpAGZNq0dV1/tuwpUXFwkp512SgAjEgkOh8PwzDNJzJ3bi8WL91JQ4GHEiFYkJen/v4gEh4YCiog0INHRLl59NZmoqBOHQDkcEBPjZN68IfqmXirl9ttvp1evXvTv358ZM2Zw+PBhr/stW7aMnj17kpCQwGOPPRbgKCt26qmRXH11V268MUFJldQ7jaWdSeUosRIRaWBmzuzI8uUjOfPM1kREOIiKcjJjRnvWrTuL4cNbBTs8aSDGjx/Ppk2b+M9//kOPHj149NFHy+3jdru54YYbWLp0Kenp6cyfP5/09PQgRCvSMKmdhRYNBRQRaYBGjmzNZ5+NCXYY0oBNmDDh2M/Dhg1j4cKF5fZJTU0lISGBrl27AjBz5kwWLVpEnz59AhanSEOmdhZa1GMlIiIS4l5//XUmT55c7vGMjAw6dDi+LEB8fDwZGRkn7OPxWOzdm8/Bg4V1HqdIQ1aTdiYNg7H8LQQBWl1PxIfk5GTS0nwXERARNNkryIwxK4BTvWy6x7KsRaX73AMkA+daJ10UGGPOByZalnV16f3LgCGWZd3k5VyzgdmldyMty+pbe69EpP5SO5OjNBRQRKQeyc938/e/7+bvf9+NZVnMmNGeSy/tREyMPq6l6izLGudvuzHmCuBs4KyTL/ZK7QHKrmQdD+z1ca4UIKWaoYo0WGpncpR6rESqST1WUtv27Mlj+PDPOHSomJycEsCu9NekiYt//3ssXbs2CXKEVaYeq3rMGDMJeBoYbVlWlo99XMD3wFlABrAeuNiyrM0BC1SkAVM7Cy2aYyUiUk+ce+6/2bu34FhSBZCb6yYrq5Czz/4XFXwRJlJVzwNNgU+NMRuNMS8DGGPaGWOWAFiWVQLcCCwHvgXe0cWeSJWonYUQ9ViJVJN6rKQ2pacfITl5Bfn5bq/bY2KcrFw5mqFDWwY4shpRj5WIiIQM9ViJiNQDmzb9QliY/zxk8+YjAYpGREREqkqJlYhIPdCyZTj+BhA4nYaWLcMDF5CIiIhUiRIrEZF6YPTo1oSF+f5ItiyYONFbNV8RERGpD5RYiYjUAy6Xg9dfTyYqylluW1SUkxdfHEhkZPltIiIiUj8osRIRqSemT2/PsmUjGT68JQ4HOByQnNycRYvO4NJLOwU7PBEREfFDVQFFqklVAaUuFRd7APwOD2wAVBVQRERChivYAYiISHkNPKESEREJOfrLLSIiIiIiUkNKrERERERERGpIiZWIiIiIiEgNKbESERERERGpISVWIiIiIiIiNaTESkREREREpIaUWImIiIiIiNSQ3wWCJ02aZB08eDCA4dS9rKwsWrduHeww6gW9F8dV57349ttv6d27dx1FFDz6f3Gc3ovjqvNebNiwYbllWZPqKCQREZF6xW9iBfjd2BAlJyeTlpYW7DDqBb0Xx1XnvWis719jfV3VoffiuGq+F6YuYhEREamPNBRQRERERESkhpRYiYiIiIiI1FDIJVazZ88Odgj1ht6L4/ReHKf34ji9F8fpvWhcjDE7jDHjgh1HdRljLGNMQrDjEKnPjDGrjDFXl/58iTHmk2DH1NiF3Bwrkdqi+TciFdIcqzpmjNkBtAHcQA6wDLjRsqycSjzvasuyVtRiLGOAeZZlxZd57BTgaWAKEAPsA16zLOvxGp7LArpblrWtJscRqanSthQFdLUsK7f0sauBSy3LGhPE0DDGrMJuk68GM45QEnI9ViIiIo3M/1iW1QRIAk4D7gpyPGX9CWgC9AZigWnA9mAFY4xxBevc0qi5gJtrcgBja1DX5WpP5TWoX6CIiIh4Z1lWJrAcO8HCGDPNGLPZGHO4dEjQyetDDDbGpBtjDhlj3jDGRJY+70pjzL/K7lh26J0xZkrp87KNMRnGmN8aY2KApUA7Y0xO6a0dMBj4m2VZhyzL8liWtcWyrIVljptojPnUGPOzMWa/Mebu0seHGGPWlsa+zxjzvDEm3NvrNsZEGGOeNMbsKj3Gy8aYqNJtY4wxe4wxdxpjMoE3av5Oi5TzR+C3pT20JzDGDDfGrDfG/FL67/Ay21YZYx42xqwB8oCupW3temPM1tI29ntjTLfS9nDEGPPO0bZgjGlujFlsjMkqbceLjTHxJ8dQuu+xdm2MuaNMO80xxhQbY/5aui3WGPNaabvLMMb8wRjjLHOMNcaYPxljfgYeqN23seELicTq3XffJTExEYfDUW7o1qOPPkpCQgI9e/Zk+fLlQYoweB544AHat29PUlISSUlJLFmyJNghBdSyZcvo2bMnCQkJPPbYY8EOJ+g6d+5Mv379SEpKIjk5OdjhBNSsWbOIi4ujb9++xx77+eefGT9+PN27d2f8+PEcOnQoiBEGjrf3ItQ/KxqC0guqycA2Y0wPYD5wC9AaWAJ8fFJycgkwEegG9ADureSpXgOutSyrKdAX+Kx0CNRkYK9lWU1Kb3uBdcDDxphfGWO6nxRvU2AF9vDFdkACsLJ0sxv4DdAKOB04C7jeRzyPl8afVHqM9sD9ZbafCrQAOgGaLCh1IQ1YBfy27IPGmBbAP4A/Ay2xh8X+wxjTssxul2H/v2wK7Cx9bBIwCBgG3AGkYLfXDtht7qLS/RzYXxZ0AjoC+cDzFQVrWdYTR9spdm9yFvBO6eY3gRLstnQaMAG4uszThwI/AHHAwxWdK+RYluXv1iikp6dbW7ZssUaPHm2tX7/+2OObN2+2+vfvbxUUFFg//PCD1bVrV6ukpCSIkQbe7373O+uPf/xjsMMIipKSEqtr167W9u3brcLCQqt///7W5s2bK/38QYMG1WF0wdGpUycrKysr2GEExerVq60NGzZYiYmJxx67/fbbrUcffdSyLMt69NFHrTvuuCNY4QWUt/eimp8VFf2N0a2GN2AH9tyqbOx50SuBU4D7gHfK7OcAMoAxZZ43p8z2KcD20p+vBP510nksIKH0513AtUCzk/YZA+w56bEo4G5gA1AMbAMml267CPi6kq/zFuCDk+PBnseXC3Qrs+104McyMRUBkcH+XenWOG+lbWkcdsLzC/YXGVdjJ1qXAakn7b8WuLL051XAQydtt4AzytzfANxZ5v5TwDM+YkkCDpW5vwp7LqWvdh1V9vjY8zULgagy+1wEfF7mGLuC/Z7X51tI9Fj17t2bnj17lnt80aJFzJw5k4iICLp06UJCQgKpqalBiFCCITU1lYSEBLp27Up4eDgzZ85k0aJFwQ5LgmTUqFG0aNHihMcWLVrEFVdcAcAVV1zBhx9+GIzQAs7beyH12jmW3Xs0BuiF3cvTjuPffmNZlgfYjd2bc9TuMj/vLH1OZZyHnYjtNMasNsac7mtHy7LyLct6xLKsQdjf2L8DvFv6TX4HfMy3Msb0KB3WlGmMOQI8Uvq6TtYaiAY2lA4bPIzdA9a6zD5ZlmUVVPK1iVSLZVmbgMXA3DIPn9AOS+3Edzs8an+Zn/O93G8CYIyJNsa8YozZWdpOvgBOOTp0rxJeA76zjheT6QSEAfvKtKdXsHun/MUrpUIisfIlIyODDh06HLsfHx9PRkZGECMKjueff57+/fsza9askBnqBPr9e2OMYcKECQwaNIiUlJRghxN0+/fvp23btgC0bduWAwcOBDmi4ArVz4qGwrKs1cBfgSeBvdgXSYA9MR47kSn7IdehzM8dS58Ddg9QdJnnnnrSedZbljUd+2LrQ44PIfJfZtiyjiZIMUAX7Au0bj52fwnYgl35rxl2r5e3KpMHsS80Ey3LOqX0FmvZQ5yOndpfXCK16HfANRxPnE5oh6U6cmI7rMn/z9uAnsDQ0nYyqvTxCiuyGmPmlj73qjIP78busWpVpj01sywrsZbibfQaTWI1btw4+vbtW+7mrweitFvzBPbfnsbF33tz3XXXsX37djZu3Ejbtm257bbbgh1uwITK778q1qxZw1dffcXSpUt54YUX+OKLL4IdktQTofxZ0cA8A4wHPgGmGmPOMsaEYV+AFQL/LrPvDcaY+NLeo7uBv5c+/g2QaIxJKi1o8cDRJxhjwo29Hk6sZVnFwBHs+VBgf6ve0hgTW2b/+4wxg0ufF4ldOe0w8B32t/unGmNuKS1A0dQYM7T0qU1Lj51jjOkFXOftxZb2xP0F+JMxJq70nO2NMROr/taJ1Ixll///O/Dr0oeWAD2MMRcbY1zGmAuBPtj/92tDU+wvFg6XtuPfVeZJxpjJpTGeY1lWfpn492F/djxljGlmjHGUFs4YXUvxNnqNpkziihVVX4ojPj6e3buP92ju2bOHdu0qOxKi4ajse3PNNddw9tln13E09Ueo/P6r4ujrj4uLY8aMGaSmpjJq1KgKntV4tWnThn379tG2bVv27dtHXFxcxU9qpNq0aXPs51D7rGhILMvKMsa8hT3h/VLgOexvzzdil2UvKrP737AvotoBi4A/lB7je2PMQ9iFJfKxy7dfW+Z5lwHPlw43+q70PFiWtcUYMx/4oXRbH+xvt9/A/pa+BPgPMNUqXWfLGDMeeBb7grAQOzH8ErsIQErp6/ga+2J1rI+XfSd2sYp1xphW2L0BL2FXSBQJtIew2wiWZf1kjDkb+//4S9hzDM+2LOtgLZ3rGex2fBC7d+wp4JxKPO9C7OGy35b5QnmeZVlzgMuBx4B07MTtB+wCMVIZFUzCalROLl6xadOmE4pXdOnSJeSKV+zdu/fYz08//bR14YUXBjGawCouLra6dOli/fDDD8eKV2zatKnSz29sxStycnKsI0eOHPv59NNPt5YuXRrkqALrxx9/PKFgw29/+9sTilfcfvvtwQot4E5+L6r5WRH0icS66aabbrrpFqhbo+mx8ueDDz7gpptuIisri6lTp5KUlMTy5ctJTEzkggsuoE+fPrhcLl544QWczsrO92sc7rjjDjZu3Igxhs6dO/PKK68EO6SAcblcPP/880ycOBG3282sWbNITEys+ImN1P79+5kxYwYAJSUlXHzxxUyaNCnIUQXORRddxKpVqzh48CDx8fE8+OCDzJ07lwsuuIDXXnuNjh078u677wY7zIDw9l6sWrUqZD8rREREKsNYlv+5poEKRKShSU5OLrcumoicILQnLTYAxpgOwFvYaz15gBTLsp49aR+DPZRpCvYipldalvVVoGMVaajUzkJHSPRYiYiIiFclwG2WZX1VumDvBmPMp5ZlpZfZZzLQvfQ2FHuuyNDyhxIRH9TOQkSjqQooIiIiVWNZ1r6j34pblpUNfMuJa+wATAfesmzrsNfJaRvgUEUaLLWz0KHESkRERDDGdAZOw67KV1Z7TlwUdA/lLwpFpBLUzho3JVYSEpYtW0bPnj1JSEjgscceK7fdsix+/etfk5CQQP/+/fnqKw1rFmmELN2833JycqyBAwf++N5773WwLOuXstumTJky9Z///Oc/j94fO3bs2LS0tPUnHyMlJcVKTk62kpOTrcTExKC/phC61TfBfj/q7U3trEHfKkWJlTR6brebG264gaVLl5Kens78+fNJT08/YZ+lS5eydetWtm7dSkpKCtdd53UtShGRRqe4uJjzzjuPSy65hHPPPbfc9squ+Td79mzS0tJIS0sjKiqqTmMWaWjUzkKDEitp9FJTU0lISKBr166Eh4czc+ZMFi1adMI+ixYt4vLLL8cYw7Bhwzh8+DD79u0LUsQiIoFhWRZXXXUVvXv35tZbb/W6z7Rp03jrrbewLIt169YRGxtL27aa+iFSWWpnoUNVAaXRy8jIoEOHDsfux8fH8+WXX1a4T0ZGhj7URKRRW7NmDW+//Tb9+vUjKSkJgEceeYRdu3YBMGfOHKZMmcKSJUtISEggOjqaN954I5ghizQ4amehQ4mVNHre1mqzl4uo2j4AKSkppKSkAJCVlVVLEYqIBMeIESO8fv6VZYzhhRdeCFBEIo2P2lno0FBAafQqM265OmObW7duXXdBi4iIiEiDosRKGr3BgwezdetWfvzxR4qKiliwYAHTpk07YR+NbRYRERGRmtBQQGn0XC4Xzz//PBMnTsTtdjNr1iwSExN5+eWXAY1tFhEREZGaMxWM+ax03XaRUJOcnExaWlqwwxCpz8pPVAwu/U0LEH0+BpTaWYhSOwuoSrUzDQUUERERERGpISVWIiIiIiIiNaTESkREREREpIaUWImIiIiIiNSQEisREREREZEaUmIlIiIiIiJSQ0qsREREREREakiJlYiIiIiISA0psRIREREREakhJVYiIiIiIiI1pMRKRERERESkhpRYiYiIiIiI1JASKxERkRA1a9Ys4uLi6Nu3r9ftq1atIjY2lqSkJJKSknjooYcCHKFIw6d2FjpcwQ5AREREguPKK6/kxhtv5PLLL/e5z8iRI1m8eHEAoxJpXNTOQod6rERERELUqFGjaNGiRbDDEGnU1M5ChxIrERER8Wnt2rUMGDCAyZMns3nz5mCHI9IoqZ01DhoKKCIiIl4NHDiQnTt30qRJE5YsWcI555zD1q1bve6bkpJCSkoKAFlZWYEMU6RBUztrPNRjJSIiIl41a9aMJk2aADBlyhSKi4s5ePCg131nz55NWloaaWlptG7dOpBhijRoameNhxIrERER8SozMxPLsgBITU3F4/HQsmXLIEcl0rionTUeGgooIiISoi666CJWrVrFwYMHiY+P58EHH6S4uBiAOXPmsHDhQl566SVcLhdRUVEsWLAAY0yQoxZpWNTOQoc5miH74HejSChLTk4mLS0t2GGI1Gf17cpAf9MCRJ+PAaV2FqLUzgKqUu1MQwFFRERERERqSImViIiIiIhIDSmxEhERERERqSElViIiIiIiIjWkqoAiIiIN2fbtkJICX3wBTiecfTbMmgVxccGNy+OBr7+GrCzo0gV69gxuPCIidUyJlYiISEO1Zg1ccw0UFYHbbT/2xhswfz68/z506xacuHJzYfhwyMkBY+zYOneGF1+0/xURaYQ0FFBERKQhKiqC66+H/PzjSdXRx7Oz4cYbgxPXli2wYwfs328nWDk5dozffQfnnQe//BKcuERE6pgSKxERkYbo889PTKjKsizYuRO2bQtsTADPPmsPAzyZxwN5ebBgQeBjEhEJACVWIiIiDVFGBhQX+97uctn7BNrq1b63FRTA4sWBi0VEJICUWImIiDREbdtCeLjv7SUl9j6B5q23qixfvWwiIg2cEisREZGGaOxYuzCEN8ZAhw7Qo0dgYwIYNsz3togImDgxcLGIiASQEisREZGGKCICnnsOIiPtMutHhYVBkybw5z8HJ65bbgGHj8uL8HC4+OLAxiMiEiBKrERERBqq0aNh0SJ77apWraBNG7j0Uli2LHjrRiUl2b1lTZpATIyd+MXE2MMSFyyA1q2DE5eISB3TOlYiIiINWY8e8MwzwY7iRM2awb//bReyOHjQXrtq6FDfPVkiIo2AEisRERGpfeHhMH58sKMQEQkYfXUkIiIiIiJSQ0qsREREQtSsWbOIi4ujb9++XrdblsWvf/1rEhIS6N+/P1999VWAIxRp+NTOQocSKxERkRB15ZVXsmzZMp/bly5dytatW9m6dSspKSlcd911AYxOpHFQOwsdSqxERERC1KhRo2jRooXP7YsWLeLyyy/HGMOwYcM4fPgw+/btC2CEIg2f2lnoUGIlIiIiXmVkZNChQ4dj9+Pj48nIyAhiRCKNj9pZ46GqgCIiIuKVZVnlHjPGeN03JSWFlJQUALKysuo0LpHGRO2s8VCPlYiIiHgVHx/P7t27j93fs2cP7dq187rv7NmzSUtLIy0tjdZaBFik0tTOGg8lViIiIuLVtGnTeOutt7Asi3Xr1hEbG0vbtm2DHZZIo6J21nhoKKCIiEiIuuiii1i1ahUHDx4kPj6eBx98kOLiYgDmzJnDlClTWLJkCQkJCURHR/PGG28EOWKRhkftLHQYb+M6y/C7USSUJScnk5aWFuwwROoz75MEgkd/0wJEn48BpXYWotTOAqpS7UxDAUVERERERGpIQwFFRESk/tqzB/71L7AsOOMM6Ngx2BGJiHilxEpERETqn6Ii+O1v4ZNPwFE6wMbjgdGj4dlnITIyuPGJiCwhhZIAACAASURBVJxEQwGlUbv99tvp1asX/fv3Z8aMGRw+fNjrfp07d6Zfv34kJSWRnJwc4ChFRMooLIQ334Rx42DQIDj/fFi50u6xCSX33AOffmq/H/n59q2wEFavhltvtfdJT4clSyA11U66RESCSMUrpFH75JNPGDt2LC6XizvvvBOAxx9/vNx+nTt3Ji0tjVatWlX62Jo0KlIhTaqvqvx8uOAC2LYNCgqOPx4VBRddBPfdF7zYqqDGn49ZWTBihN1r5U14OLRvD5mZ4HTaSWd0NDzzDAwfXv3zNkxqZyFK1yEBpeIVIhMmTMDlske8Dhs2jD179gQ5IhERP155BbZuPTGpAjvhmj8fvvqqcsf5+Wf4859h/Hg480x44AEoswBpvbdhg508+VJUBDt22O9LTg7k5trJ2FVX2b1YIiJBoMRKQsbrr7/O5MmTvW4zxjBhwgQGDRpESkpKgCMTESn19tv2cDdvCgrgr3+t+Bg7d9oJ1Ysv2j1fO3bA3/4GEyfaQ+YaAkclLk+8jbgpLLR7reoztxtWrIBHH7Xnin33XbAjEpFaouIV0uCNGzeOzMzMco8//PDDTJ8+/djPLpeLSy65xOsx1qxZQ7t27Thw4ADjx4+nV69ejBo1qtx+KSkpxxKvrKysWnwVIhLyLMvuafK3fefOio9z001w6NCJiUdxsX279lo7uQoLq3m8taWwEJYtg0WL7BjPOstODEsXUK0Sy4J//rP2Y6wtu3fDzJnwyy92L5vTCS+/DGPH2glhffq9iEiVKbGSBm/FihV+t7/55pssXryYlStXYoz3IbLt2rUDIC4ujhkzZpCamuo1sZo9ezazZ88GUJELEaldxkCLFr6TK4cDOnf2f4zNm+2br/nTxcXw+ecwYUKNQq01Bw/C//6vPYwvL89+bMMGuydn2jRYvNge7leWMf4LeXg88NJLsHy5/Z79z//Y52jatO5eR2W43fY8uczM44U23G779tln8Mc/wt13BzdGkaqwLFi/HubNg717oUcPuPJK+98QpaGA0qgtW7aMxx9/nI8++ojo6Giv++Tm5pKdnX3s508++YS+ffsGMkwREdvll/suIx4RYV+0+DN3rv/qeHl59hC0d98tP4+rpjIz4Q9/sNea2rIF7rgDtm/3/5xbboGMjONJFdiJ1C+/2GtXXXGF/X40bWrfIiLgtNP8z7/yeOz5Zd98A19/bScsY8cGf47Z6tVw+LD3309BgX1xenISKVJfWRbceaf9mbR4sf2FyDvvwPTp8NprwY4uaJRYSaN24403kp2dzfjx40lKSmLOnDkA7N27lylTpgCwf/9+RowYwYABAxgyZAhTp05l0qRJwQxbRELVtdfa3/ZGRZ34eFQUXHqpnVT48v339s0fy7LnXD3wgF3O/cCBmkZs++47uxfsrbfsb66Li+H99+3eojVrvD8nIwPS0qCkxHucR47YSdr69fDcc3aylJoKKSm+k09j7OOVTRrz8+1ewOuuq/nrrIkNG+zhf744nfDDD4GLR6QmPvjgeI/y0R5kt9tue489Zn+pEYI0FFAatW3btnl9vF27dixZsgSArl278s033wQyLBER7yIj7W9933vPTlIOHYJu3eyEa/Ro/8+tytyivDx7btNNN8Hf/16zmME+Tk7OiUP03G77ouv66+0E6uT5Q9u22T1Pvop1FBfbieKIEfZr93jsHp+oKDvmq68+3gPkdNr/ut3ej+fx2EnL998Hb5hSVJQdp9vtfbvbrUWPpeF46SXfPawlJfZcwnXroHnzwMYVZEqsRERE6pOICLj4YvtWFT7mkPrkdtvD5Xbtgo4dq/bcsr77zh5m52vek8fjfV5X8+a+kwywE7FTTrGfn5Ji33Jz7ftDh8Krr9pDBnfsgFatYN8+eOQR38dzOu2hicFKrCZPhhde8P2aW7aErl0DG5NIde3a5X97UZFdiKayS0Q0EhoKKCIi0hiMGlW5MuVlhYdXPA+qIrt2+e51AntoUEZG+cf79YNmzXw/z+22qwPeeqs9DPDQIftiraTEHl543nkQGwsXXmhfwMXF+X/9Ho9d2GPKFHtI5aRJdu+gt6GIdaFbN7vk/cnDPMHuqXrwwaonxyLBUpliMIcOwQ031H0s9YgSKxERkcYgIQFOP93u8aosj6fmQ3V27vRfpc/thvbtyz9ujF1Ywtvwt8hIu0Lejh3wySfehxzl5dnJyFGjR/tPTEpK4PXX4dtv7SGE331nzzW7+urAJVdPPWVP9o+JsW9RUdChg73m2NixgYlBpDZceGHlvshZssTuTQ4RSqxEREQaixdesC/Qw8OPX7z707QpDBhg9wSlpMCwYXaCNniwXTCiMpUDN23yv92yICnJ+7YRI+zFi4cNsy/SHA7o2dM+92WX2RPk/fWGffmlPX8L7ITyiSfKJ2rG2MMKjSmfoOXn2wUxPvzQ/2uoLU6nXS1xwwZYuNC+6Fy9Gs48MzDnF6kt114LTZpUbt8zzqjbWOoRJVYiIiKNRVSU3fuxapVdVv3JJ+1eGW+9QlFRLPvf/6Vnz54ktGzJY/feC/v32z1MBw/Ciy+y6swziY2NJSkpiaSkJB566KGqx+Ry+V/49rTTYP58uwfp22/txYLHjbO3/fyz//LxlmUnYKtX2/cnT4a337Z77o6ed8wY6NLFd4KWn2/P1wqkiAjo1ctel0zD/0LCsmXL7LaWkMBjjz1WbvuqVatq3tYCqVkze9mGyrCsE3uXGzEVrxAREWls2ra1S50f1bmzPQxt0yb7Qv7003Hfeis3nH8+n952G/HPPcfg7duZFhZGn6NrRJXOjRrZuTOLN270fa4JE+DTT09ci6qsTp0qN9zQ5eWS5LTTfA8FPKqgAG680e4FCg+H5GS7F6ysYcP8n3v//orjE6kmt9vNDTfcwKeffkp8fDyDBw9m2rRp9OnT54T9Ro4cyeLFi4MUZTX06GEXXKnMMgF//Sv87nd1HlKwqcdKRESksRs9Gj76CLZutW/z5pFaWEhCQgJdly4lvKCAmTExLDo5OSoqgj17/B97/Hi7Kp/TWX5bZCSMHAlz5tiT2JcsscuoV9aMGZWbx5GbC08/7Xu7tzleZbVrV/mYRKooNTXVbmtduxIeHs7MmTNZtGhRsMOqHcuWVWo3ywJuu61uY6kHlFiJiIiECqfzWKKSkZFBhw4d4KefAIh3ucjwUgp87S+/MGDAACZPnszmzZvLHzMsDN59l5SmTUnOzCQ5M5Msy7KHJZZuY/lyO6m64w67Ml7pOSvUrBm88UbFBTksyy5M4WtNrquvhuho79uio+3tInXkWFsrFR8fT4aXSplr167139aAlJQUkpOTSU5OJisrq85irrSwsPJLKXjhAQ69u6Tu4wkyJVYiIiIN3c8/22tJFRVV+inW0Up+3bode+zk2T4DIyLYOXEi33zzDTfddBPnnHOO94PFxTH7669J++Yb0ubPp3XnznDqqXZPUm7u8f1yc+04b7qp0nEyeDDMm+d/nhbYPWG//733uVSTJtkl2U9OrqKj7QIa06dXPh6RKrK8VM00J82tGzhwIDt37qywrc2ePZu0tDTS0tJo3bp1ncRbZa+8UuEuTgNOY5H16ZcBCCh4lFiJiIg0VJs22cPlhg2zk4eBA+3KeJUYbhcfH8/u3bth9myIimJPSQntThrO16xJE5qUrkMzZcoUiouLOXjwoO+D9ugB55xjz5fav9974YmSEnvR0IoWGC0rOdmeJ1YRY+w1rrw9/uyzdjGPQYOgTRu7UuHjj8NLL1V9/S+RKjjW1krt2bOHdicNP23WrBlNSqvsVaqt1TeVWC4g3JTwu1/9IwDBBI8+SURERPzJz4c337QTl+HD4Zpr7EIJwbZ5s72WzMaNdiKVl2f3CL3xBlx3nf+1pYDBgwezdetWfoyPp2jmTBbk5zOtbHn2qCgyzzwTa9IkwJ4n4vF4aNmyZcWxFRT4r+YXHg5btlTmVR730kveF9cty7JO7CE7as8eu+rgmDF2mfN16+xS7mefraRK6tyxtvbjjxQVFbFgwQKmTZt2wj6ZmZnHeraq1Nbqi9deA6DEz8fON0Xt+GBfRwoKyg85bixUFVBERMSX7Gw47zz7wvxoZbrMTLtX5Le/hVmzghfbQw95r8RXUAD//jd8/bXdg+WDy+Xi+eefZ+LEibjdbmbNmkWiMby8ahW0aMGcJ55gYVoaL/Xrh8vlIioqigULFpQbwuSV0+m9mMVRlgWxsRUfp6xu3ex5VJdd5ntBX7cbylZa+/JLuPde+/fnctnbL7wQ7rrLTu5EAsBrW0tM5OWXXwZgzpw5LFy4kJdeeqnqba0+OftsCj9ajsuU7zHP9YTx4OFxFFhhtG//MT/95GNYcQNnvI37LMP/110iISw5OZm0owtTiog39e2qoOp/0+6/3y6I4G3uUkSEXZShU6daCK2KsrPtIW2+hvwZAxdfDH/4Q2Dj+uor+MtfSJ43j7TWrX33mjVvDuvX+0++fJkxwx4CeXJy5XLZ5dnfece+v349XH55+UWOIyPtoZOvv95Y1pCqby9C144BUh+vQ+6Lnc3cU1ZhYWjiKCLfY/fh3H94As8dOQM3hhJcWNb5QY60yirVztRjJSIi4k1JiT1szFdBCI/HXi/prrsCGxfYvWf+hrBZFhw5Erh4wF6Y+Pnn7USmqMh3UhUZaS9eXJ2kCiAlBS64ALKyjg/7i4mx50299NLx/R58sHxSBfZjX35pD6E87bTqxSAiXkVfej7dXh7KjJjN9A7bT0ZJLPNyB7LX3QwHFp7SWUjbth0hIaFZkKOtfUqsREREvMnJsYeO+VJcDNu2BS6eslq2tOcbeauAB/a2wYMDF096Ojz3nPdExhj75nBAz552InrGGdU/V+vW9oLEn31mLx7scNjlns8883iy9tNP9npdvhQW2ut6KbESqVV3PncWD744nzeyk3EYiwLLhVXa2eMpU9ph+vQ1bN48OVhh1hklViIiIt7ExPgfKuZyVa5SXV1wOu1qfs89d3zuV1kulz1kLlD++lffwxItyx7698UXdnnz/Hw7Ya1ujxXYr2/CBN/r5xQU+D++x2MnziJSqxwOB4WUzl/0Myg0Pb1xtj+VwhEREfEmLMwuHe5r/SSXy57HFCzXXmtXKoyMPD4sMDoamja1qxiWlm4OiG3b/Pfu/fwz/OUvdjGN/v0hMdFeLLiuFjht08b/osLR0fY8KxGpdddf37lS+x086KWHu4FTYiUiIuLL3Ln2QrcnX6RHRcH115+wuG7AORzw9NPw3ntw1VX2vKP77rNLiQd6iFv79v5795xOexHRw4ftBKyw8Hi587pYq8flssvi+yrPHhEBU6fW/nlFhBdeqNww5H/8Y18dRxJ4GgooIiLiyymnwOLF8NZbsGCBXSyhZ087qRo1KtjR2fr0ObHEeDBcdhmsXOl7WCKUn39V8v/t3XtYlHXeP/D3PTMgDIgnFMFBEVFAUMnAtMxMOSgoZj7rIftVj1toa2sHD2uWl9am0nlb3TWpXM3LR8t9NDyi4mHXPLHYg4qUopLBIIqpyGE4zM39+2OExDkwODDH9+u6uGLu+zsznyE/MJ/5fu/PVwvcuqVreLF0aevHNGsWcOUKkJ6uK+a0Wt3yTg8PYONG3X+JqE24uxvv+9PAGfezYrt1ogdkj21OiewM20C7CknSNaXYvh3QaBBdXIzsgADdzJBMZrjgauDtDZw923axXb6sK44rKnTLEOPjnW0PK+aZi7Ln9yGffvoT3njDdF77+bmjpGSClSKymFl5xqWAREREZBlB0LVQT00FwsJ0xZSfn25mr3dv0/c1tMlxawoOBubMARYt0i09dK6iisguvf56WLNjrl2rRTMTPA6HhRURERFZThCA5GRgzx5dc4oTJ3QFzaBBpjv0BQZaL0Yishpz9t/etUvd9oFYEQsrIiIiajszZhjvrOjpCbz8snXjISKrUJjRyWH79pK2D8SKWFgRERFR2wkJAd59V9csoqHAksl0RVVSkq6bIRE5neDg5rd8KC42cf2lA2JXQCIiImpbv/udbt+oDRuAc+cAf3/dHmAPPWTeeiEicjhLlvTHM89kmRxz40YzrQMdDAsrIiIiV3bjhq4l+dWrukYP48frNhlubYGBugYSROQSEhP9mx1TVaW1QiTWw8KKiIjIVX39NbB8ue77mhrd8rz33tPtLTVqlG1jIyKH5uPjBrlcgCga7/zXr18bfIhjQ7zGioiIyBUdPaprj15To/sCdPtNaTTAK6/o9n8iInpAgiBg+vSeRlf7KpVyzJzZx7pBtTEWVkRERK7or39Fxq+/IrSoCCFFRUi9ffu3c3V1wNq1kCQJc+bMQUhICAYOHIgffvjBdvESObCMjAyEhoYiJCQEqampeuedNdeWLx8AX193vR0XlEo5nnyyG2Jju9kmsDbCwoqIiMiFXLtWjUOHriM7qxSzb97EHj8/5PXogU2VlcirvXshuVYLHD+OPXv2ID8/H/n5+UhLS8PLbI1O1GKiKGL27NnYs2cP8vLysGnTJuTl5TUZ46y51qOHJ374IQ7TpvVEu3YyKBQCfH3dsXhxOL777lEITta8htdYERERuYCysjr8939nYffuEnh4yFFzZwQgXcNFbQ8Eu+VjqpcX0quq0N/dXXcHpRLp6el47rnnIAgChg4ditu3b+Pq1avw92/+onQi0snKykJISAiCg4MBAFOnTkV6ejr69+/fOMaZc02lUmLDhkewbt0QVFeLUCrlTldQNeCMFRERkZMTRQlPPHEIu3aVoKamHmVldaiWylGN7njq+vM4Uh0ElUIBtSjq7uDpCfzud1Cr1QgMDGx8HJVKBbVabaNXQeSYzMkjV8g1uVyAl5fCaYsqABAkyXinDgAmTxK5sujoaGRnZ9s6DCJ7Zld/PSMiIiRPT09bh9EipaWl6Nq1q8WPU1ZWh8uXK1Fff++f9QoAGgBd4SXUoqu8BJX19ejp5qbbyLdvX+RfugR/f394e+s2+rxw4QJUKhWUSqXBWG/cuAEAqKmpQVRUlMVxW1Nr/ayt7dSpU+ckSYq0dRwNHDHPgLb9/3/r1i3cuXMHvXr1AgD8+uuvqKysRM+ePRvH5Ofnm5Vrjp5ngGPmmrl5xqWARETkEjw9PR3uw5DW+gDnmWdO4OLFwvuOXgCwBcBbqJFEPN/pDbST6vDm00/rWrB36YKZM2di5MiRmDZtGgAgNDQUBw4caHZ5kpeXl8v+rK1NEIRqW8dwL0fMM6Bt//8fP34cS5cuxd69ewEAK1asAAC8+eabjWMeJNccMc8Ax8w1c/OMSwGJiIicXE1NvYGjfQBcBXAdgkLCPzu0R/L27cCaNUCXLgCA5ORkfP3115AkCSdOnECHDh2c4poPImuKiYlBfn4+CgoKUFtbi82bNyM5ObnJGOaac+CMFRERkZNLTPTH3r0lqKwU7zkqBzADwDJIkDD1+dmIGDYMq1b9HadP30ZNzWjI5V0hk/khJCQESqUS//jHP2z0Cogcl0KhwKpVq5CQkABRFDFjxgxERETg888/BwDMmjULiYmJ2L17N3PNwbGwIiIil5CSkmLrEFqstWKeNi0Qb755BlVVIppeWj0YSmUM1qx5GM8+2wuFhVX48MNeuHnTHxUVvwAA2refgF69puFf/3oSnTu7m/V8vr6+rRK3NTniv4+70mwdwL0c9efY1nEnJiYiMTGxybFZs2Y1fi8IAv72t7+16DEdMc8Ah/03YlaesXkF0QNyxDXCRFZmV80r4OJ/03766Q4SEv6NW7fqoNGIcHeXob5ewuLF/bFoUTgkScKgQfuQl1cOUWz6o3J3F/Dkk92QkTHCrOfi70erYp65KOaZVZmVZ5yxIiIicgFhYT4oKEhCZuY15OTcRseO7nj66R7w9W0HAPjPf27h8uVKvaIKAGprJfzrX6W4cqUSvXp5WTt0IiKHwOYVRETkMubPn4+wsDAMHDgQEydOxO3bt20dUrO2bNmCiIgIyGQyiz+dlskExMd3x4IFYUhJCW4sqgDghx9uod5Qj4u73N1lOH26zOTjZ2RkIDQ0FLm5uUhNTbUoVmuZMWMGunXrhshIu+lY3qzCwkI8+eSTCA8PhyAI5wRBeNXWMd3L1fPMGjIyMpCbm4uQkBDmWhtqyDVBEH40J9dYWBERkcuIi4tDbm4uzpw5g379+jW2PbZnkZGR2Lp1K0aMMG8Z3oPy8XGDQmF6tYuPj/GFLqIoYvbs2dizZw8iIiKwadMm5OXltXaYre6FF15ARkaG2ePr6yVs2HAFAwbsRfv22xAUtAsffngeGo3Y/J1biUKhwMcff4wff/wRAIYCmC0IQn+rBdAM5lnbasi1vn37Ii8vz2lzzR405JokSeEwI9dYWBERkcuIj4+HQqErDoYOHYqioiIbR9S88PBwhIaGNt6+fLkC8+efRlLSEfzhDz/gzJnWmQ0YN84fWq3xy2M0GhGnTt3CL79UwtD12VlZWQgJCUFwcDAEQcDUqVORnp7eKrG1pREjRqBz585mjZUkCdOnn8TLL59Cbu4dVFRoceVKFZYsOYdHHz2IqiptG0er4+/vj8GDBzfEVA7gRwA9rPLkZnCGPLNnDbnWrl07uLu7O2Wu2YuW5hoLKyIicklr167F2LFjbR1Gi/zzn4WIjNyLzz7Lx+7dJUhLu4ShQw9i/vzTFj+2j48b3nsvEp6eht8a1NVJmDfvDHr12g0/v+1YvfpSkwJLrVYjMDCw8bZKpYJarbY4LnuSkVGCHTuK72tbrys6f/rpDj79NN/qMQmCEATgIQAnrf7kZnDEPLN3rpBr9sicXGNhRURETkUQhExBEHLv/7r3E91ly5ZBoVBg+vTpNoz0N7GxsYiMjNT7ujfm8nIt/vKXfGg09air0xU0oqh7U79y5UUsXXrO4uVob7zRD6NH+0Fopv9VaWkt5s07jddf/62gMzSLJTT3QA7mr3+9qFdUNaiursff/37RqvFUVFQAwP8CeE2SpDvWfG5nzTNH4Aq5Zm8EQfCGGbnGroBERORUJEmKNXYKANavX4+dO3fiwIEDdvNmJDMzs9kxRUVVqK013F2ipqYef/5zHj7++AKWLOmPuXP7PdBru3ChHJmZ12B6JxadqioRa9Zcwpw5IQgO9oZKpUJhYeE98RYhICCgxTHYs8LCKpPnb9yotVIkQF1dHSZNmgQAGyVJ2mq1J77LWfPMEbhCrtmTuro6QFdUNZtrnLEiIiKXkZGRgffffx/bt2+HUqm0dTgtUl6uNVnw1NcDFRVaLFlyDn/5S9MlaZKka5c+adIxPPTQfkyZchzHjt3Qe4x16342eZ3V/URRwqZNuo2EY2JikJ+fj4KCAkiShM2bNyM5Odnsx3IEoaHtTc7m9ejhYZU4JEnC73//e4SHh0OSpE+s8qQt4Mh55ggacq2mpga1tbVOmWv2oiHXAPxoTq6xsCIiIpfxyiuvoLy8HHFxcYiKisKsWbNsHVKztm3bBpVKherqnwCkAlhmcnxVlYilS881zm5JkoSZM08hIeHf2LpVjZyc2/j22yKMGvUvvWuz1GpNiwqrujqpcZZGoVBg1apVSEhIQG5uLiZPnoyIiIgWvVZbmDZtGoYNG4bz589DpVLhq6++Mjr29df7wdNTbvCcUinHG2/0a6swmzh69Cg2bNiAgwcPQhCEnLtfiVZ5cjM4cp4dP34cSUlJSEhIsHVIRjXkWn5+PsLDw50y1+xFQ64BGGVOrgmG1mneg7tnExnBHc+JmmUf639+49B/095++yyWL//JrGV6Pj4K7N79OB57zBfffPMLnnnmpME9qtzcBOzcORzx8d0BAB9/fB6LF+dCozGxodU9vL0V+PzzwZg+vVeT4878+3HhwjNYufIiqqp+u9bKy0uOxx/3xY4dw6FQWP0za+aZi3LmPLNDZuUZZ6yIiIjs3LlzZfj00wtmFVUNRFE3eN68M0Y3/q2rk/D227mNt194IahF18O4uQmYNEllflBOIDV1IHbtGo5x4/zRp48Xhg/3xVdfRePtt/tj7dqf8e23haiosE7bdSKyL2xeQUREZOeWLs0zexYJALRaCYMHdwKgW95nSm7ubw2uunRph82bh2Lq1BPQautRW2u4kvPykkOhkGHfvhHw8DC8NM6Q69ersXfvNYiihCee6Irevb3Mvq89GTmyG0aO7AYAKCioRFLSEfzySxXq6wGFQoAoSvj440GYNauPjSMlImtiYUVObenSpfjiiy/QtWtXAMDy5cuRmKi/NDYjIwOvvvoqRFHEiy++iIULF1o7VCIio3buLDZ7tkqplOOVV0Lg7a37E9/c/e6foBo/PgC5uQlYuTIfR47cQIcObnj22Z7w9JRh584SlJdrMXp0Nzz3XBA6dHAzKyZRlDBnzv9h7doCKBQCJEl3LD6+OzZtegRKpWO+HamuFvHYYwdx7Vq13qzg3Lmn0b27B556ym727SWiNuaYv8mIWuD111/HvHnzjJ4XRRGzZ8/G/v37oVKpEBMTg+TkZPTv39+KURIRGVdTY3q2ShB0BZVWK+G553ph+fIBjeeUShmqqozfPyLCR+9Y795e+OSTKL3jU6b00jtmjj/96QzWrfsZ1dVN49i3rwSTJ5/Azp3DH+hxbe3bbwtRXl5ncKllVZWIRYvOsrAiciG8xopcXlZWFkJCQhAcHAx3d3dMnTrV4TYLJCLndebM7WY37B02rAs+++whFBQkYvXqhyGX/3aHOXP6Gr2/TAZ88MHAVoxW3507dfj73y81afbQoLq6HgcOXMOFC+VtGkNb2bZNjYoK45sy5+dX4M6dOitGRES2xMKKnN6qVaswcOBAzJgxA7du3dI7r1arERgY2HhbpVJBrVYbfKy0tDRER0cjOjoapaWlbRYzEVGD3Nw7zV7HNGVKIH7/+97w9/fUO/f22/0REeEDhaJpdaVQMsWvtAAAE2xJREFUVMDf/yO89NJjiIuLM/j7EQCCgoIwYMAAREVFITo6usXxnzx5E25upivDAweut/hx7YE5HQBlMntr2kfWdPPmTcTFxaFv375tmmdkH1hYkcOLjY1FZGSk3ld6ejpefvllXLp0CTk5OfD398fcuXP17m9oywFjXbFSUlKQnZ2N7Ozsxuu2iIjakq+vu15RdC93dwEffXQePXrswLhx3+PIkaYf+nh5KXDy5Gikpg5AcLAXOnVyw5AhnZGUlIXZs59Cfn4+Ro8ejdTUVKPPcejQIeTk5DxQa+fmZtsEQWgcU1MjYuPGK0hI+DdGjDiE5ct/RGlpTYuf01omT1Y1XstmyKBBHUyeJ+eXmpqK0aNHt3mekX1gtpPDy8zMNGvcSy+9hHHjxukdV6lUKCwsbLxdVFSEgICAVouPiKglKiu1WLfuZ3z5ZQEqKrR49NHOJsfX1kooLNR1/rt69SoOHbqOd9+NwNy5oY1jlEoF5s4NbXIsNPQPWL36MADg+eefx8iRI/H++++3+usZNqyLyU2HJUlCQkJ33LxZi0cfPQi1uqpxeV129i2kpv6EzMwnMGSI6Z+DLTz1VA8sWpSLK1cqUVfX9DV6esrx/vttu8yS7F96ejoOHz4MoG3zjOwDZ6zIqV29erXx+23btiEyMlJvTExMDPLz81FQUIDa2lps3rwZycnJ1gyTiAgAcPNmLaKi9mPBgjPIybmNixcrsHFjITQaEe7uzf/JliRd04TFi88hP9/0dUvXrl2Dv78/AMDf3x/XrxtejicIAuLj4/Hwww8jLS2txa/Jy0uB+fNDoVTqL2f09JRj/Hh/FBVVYdiwA8jPL29yzZJGI6K8XIuxY4+gttb8dvPW4uYmw9GjT+LRR33h4SGDj48C7dsr0KWLOzZsGILRo/1sHSLZmLXyjOwDZ6zIqS1YsAA5OTkQBAFBQUFYs2YNAKC4uBgvvvgidu/eDYVCgVWrViEhIQGiKGLGjBmIiIiwceRE5Ir++Mf/05v9EEUJoqhb8hcT0wmnTt2CJOmW2Bnb+FerrceaNZeRkzMXJSUleueXLVtmdkxHjx5FQEAArl+/jri4OISFhWHEiBF649LS0hrfEN5/DeqSJf3v7u10ofF6q9raekyY4I/z5yswZswRg80tGtTW1mP79mL813/Z32bE3bp54PDhkbh0qQJnz5ahY0c3DB/ua9b1V+QcYmNj7SLPyPYEQ9eX3KMFe7wTuZbo6GiugyYyzd6u2rfrv2kVFVp07Zqu15K8gaenDO+9F4lXX+2HmzdrERi402Qb9nHj/LFjh/E25qGhoTh8+DD8/f1x9epVjBw5EufPnzcZ49KlS+Ht7W1yCwvA+O/H8vI6HD5cCq1WwmOPdcGkSceQlXXT6EbEDQQBeOedCCxezG0wDGCe2TFb5Bm1CbPyjB+nEBER2YGrVzUmZzk0mnrk5ZVDLhfQqZObyW5zCoWAkBBvk8+XnJyM9evXAwDWr1+PCRMm6I2prKxEeXl54/f79u0zuKTaXO3bu2H8+ABMnNgD167V4NSp280WVYBuyWCXLu4P/LxEtmKLPCPbYWFFRERkB7p2bYe6OuMzUO3aydC7txKArs33c8/1MnrdlZubgFmz+ph8voULF2L//v3o27cv9u/fj4ULFwLQLZVOTEwEoLs+ZPjw4Rg0aBCGDBmCpKQkjBkz5kFenp7jx39ttmNgA1GUMHlyYPMDieyMrfOMrIvXWBEREdmBjh3dERvrh4yMEoiioW0ggOefD2q8vWLFABw4cB1FRVVNlg8qlXIsWhSO0ND2Jp+vS5cuOHDggN7xgIAA7N69GwAQHByM06dPP+ArMs3TUw6ZGR/vKpVyvPtuBHx927VJHERtydZ5RtbFGSsiIiI7sXr1YHTu7K43E6VUyrFs2QCoVMrGY506ueOHH2LxzjsRCA72gq+vO0aN6obt2x/DW2+FWzv0Fhs7trtei/L7RUb64H/+55EmbeKJiOwVZ6yIiIjsRGCgEmfPxuPDD89jw4Yr0GhEPPRQR7z9dn/Exem37m7f3g0LFoRhwYIwG0RrGV/fdnjttb5YufKiXkdApVKOFSsiMWdOPxtFR0TUciysiIiI7Iifnwc++mgQPvpokK1DaXMrVgxA587uWL78J9TXS5AkwMNDhuXLB+Cll4JtHR4RUYuwsCIiIiKbEAQBCxaE4bXX+iEv7w7kcgH9+/tALre3DuJERM1jYUVEREQ25e4uQ1RUR1uHQURkETavICIiIiIishALKyIiIiIiIguxsCIiIiIiIrIQCysiIiIiIiILsbAiIiIiIiKyEAsrIiIiIiIiC7GwIiIiojZVUFCJ06dvo6JCa+tQDDp58ldMnXoCUVH7MH7898jIKIEkSbYOi4gcDPexIiIiolYhihIuXChHdbUIUZRw7NgNzJx5Cj//XAU3NwF1dRJeeCEIn3wyCB4ecluHCwB4551z+OCD86iuFlFfD5w+XYZDh65j/PgAbNz4CGQyblZMROZhYUVEREQW++KLy3jrrVxoNFpUVZWjS5fvoNGIqK3VzfxoNLpx69YV4MKFcuzfPwKCYNui5dixG/jgg/OoqhKbHK+sFLFjRzG+/voKXnghyDbBEZHD4VJAIiIissgnn5zHa6/loLS0BhUVIurrJZSVaRuLqntpNPU4ceJXfP/9DRtE2tRHH12ARiMaPFdZKeL993+yckRE5MhYWBEREdEDq6zUYvHic3qzPqZUVYnYuPGXNozKPGfP3oapS6l++aXKesEQkcNjYUVEROSCtmzZgoiICMhkMmRnZxsdl5GRgdDQUISEhCA1NVXv/MGD16FQtGxJnyQB5eV1LY65Nf3nPzdRUFBpckzHjm5WioacVWvlGTkGFlZEREQuKDIyElu3bsWIESOMjhFFEbNnz8aePXuQl5eHTZs2IS8vr8kYjUY0OetjiJeXHLGxfg8Sdquora3HmDFHIJqYZPPwkOHll/tYLyhySq2VZ+QY2LyCiIjIBYWHhze5XV8vobpahKenvLGpRFZWFkJCQhAcHAwAmDp1KtLT09G/f//G+8XEdEZdXX2LntvDQ44pUwItfAUP7rvv1M3GHBTkhdde62eliMhZ3Z9nhpiTZ+QYOGNFRETkwrRaCR988BN8fLbBx+c7dOqUjkWLzkKjEaFWqxEY+FsBpFKpoFarm9y/d28vjBzZDXL5AQAL737dAQDIZLovNzddoebtrUC3bu1w6NBIKJW2+2w3N7cM5eXG99Rq107A6tWD4e3Nz5+p7ZmTZw3S0tIQHR2N6OholJaWWitEMhN/YxARETmp2NhYlJSU6B1ftmwZJkyYgLKyOpw6dQsnT6qh1eo+LS8rq8Onn15AZuY1vP66/lo5Qy3SN28eioSEWuTmxqOqSoQkLYS3txyhoe2RlhaNjIwSlJXVITq6EyZM6AF3d9t+ruvr2w6ennKjHQFlMhkCA5VWjoocVXN51hxDm1Eb24ogJSUFKSkpAIDo6OgWRkptjYUVERGRk8rMzDR5/rPP8lFTI+q9sauurkde3h3k5bmhsLCw8XhRURECAgL0HqdDBzccPz4Kx479il27ruIf//DAN988jscf94UgCBg8uFPrvKBWMnlyIP70pzNGz/fp44U+fbytGBE5subyrDkqlcqsPCP7x6WARERELiot7bLRxhOVlSIyMryQn5+PgoIC1NbWYvPmzUhOTjY4XhAEPPaYL5YvH4AePTwxYkRXm28AbEz37h5YsqQ/lEp5k+OCoGus8dVXMTaKjFxRTEyM2XlG9o2FFRERkQvatm0bioufB3ABQCqAZXfP3ASwQvfdTRGrVq1CQkICwsPDMXnyZERERLR5bGq1BvPnn0ZQ0C4EBu7EzJnZuHixolWfY+HCcKxfPwTh4e0hCIBcLmDs2O44dmwUhgzp3KrPRa5r27ZtUKlUOH78OJKSkpCQkAAAKC4uRmJiIgBAoVDYJM+o9QmG1nXeo4UNVIlcR3R0tMk9KYgI9jZdwb9p94mJyUR29i2D5wQBePrpHvjnPx9t8eNa8vsxN7cMw4cfgkYjorZW17lPoRDQrp0Mu3Y9jiee6PpAj2uKVlsPmUyATGZv/2TNYm9BM8+shO9DrMqsPOOMFRERkYtatCgcXl5yg+c8PeWYPz/UyhEBkycfR1lZXWNRBeg6F1ZWipg06Ri02pa1djeHQiFz1KKKiOwICysiIiIX9dRTAUhJCYZSKYfs7jsChUKAp6ccf/5zBB55pItV4zl9+jZ++aXK6Pna2nrs3XvNihEREZmPXQGJiIhclCAI+OSTKDz7bC+sXn0JBQWVCA/3wezZfRAW5mP1eK5cqYJcbnzmqK6uHleuVFoxIiIi87GwIiIicnGDB3fCF1/Yfk+cnj2VEEXjl+i4uXF/KSKyX1wKSERERHZh0KAO6NHD0+h5hULAmDHdrRgREZH5WFgRERGRXRAEAd9+Oww+Pgq4uf22JFAuF6BUyrFlyzC4ufGtCxHZJ/52IiIiogciSRK+//4GvvqqADt2FDfp5PegBg3qiDNn4jFzZh8EBHjAz68dpk/viezsWIwe7dcKURMRtQ1eY0VEREQtdu5cGcaPP4rS0hrU10uQywUIArB+/RCLH7tXLy+sXPkQVq58qBUiJSKyDhZWRERE1CK//lqDxx8/hNu36yDd12ti+vST6NlTa5vAiIhsiEsBiYiIqEW+/PIyqqtFvaIKAKqqRKjVGusHRURkYyysiIiIqEW2blVDozF+PVVFBWesiMj1sLAiIiKiFhEE45v4EhG5KhZWRERE1CJTpgRCqZQbPCcIgI+Pm5UjIiKyPRZWRERE1CIzZvSGl5cCMgPvIjw95QgIML7JLxGRs2JhRURERC3SoYMbTpwYhQEDOkCplMPHR4H27RXw82uH9PTHjM5mERE5MxZWRERELmjLli2IiIiATCZDdna20XFBQUEYMGAAoqKiEB0d3Xg8ONgbOTnxOH58FL78Mho7dw5HcfF4xMZyE1+iBpbmGTkW7mNFRETkgiIjI7F161bMnDmz2bGHDh2Cr6+vwXMDB3bEwIEdWzs8IqfQWnlGjoGFFRERkQsKDw+3dQhETo955lq4FJCIiIiMEgQB8fHxePjhh5GWlmbrcIicEvPMOXDGioiIyEnFxsaipKRE7/iyZcswYcIEsx7j6NGjCAgIwPXr1xEXF4ewsDCMGDFCb1xaWlrjG8LS0lLLAidyIMwzasDCioiIyEllZmZa/BgBAQEAgG7dumHixInIysoy+IYvJSUFKSkpAMCL78mlMM+oAZcCklObMmUKoqKiEBUVhaCgIERFRRkcx248RET6KisrUV5e3vj9vn37EBkZaeOoiJwL88x5sLAip/bNN98gJycHOTk5mDRpEp5++mmjYw8dOoScnByT7VCJiJzFtm3boFKpcPz4cSQlJSEhIQEAUFxcjMTERADAtWvXMHz4cAwaNAhDhgxBUlISxowZY8uwiRwK88y1CJIkmTpv8iSRo5AkCT179sTBgwfRt29fvfNBQUHIzs5uUZvT6OhoFmFEpgm2DuA+/JtmJfz9aFXMMxfFPLMqs/KMM1bkEo4cOQI/Pz+DRRXAbjxEREREZBk2ryCHZ043nk2bNmHatGlGH4PdeIiIiIjIElwKSE5Pq9WiR48eOHXqFFQqVbPjly5dCm9vb8ybN8/kOE7BEzWLS5RcFH8/WhXzzEUxz6yKSwGJAF0b1LCwMKNFFbvxEBEREZGlWFiR09u8ebPeMkB24yEiIiKi1sRrrMjprVu3Tu9YQEAAdu/eDQAIDg7G6dOnrRwVERERETkTzlgRERERERFZiIUVERERERGRhVhYERERERERWYiFFRERERERkYVYWBEREREREVmIhRUREREREZGFWFgRERERERFZiIUVERERERGRhVhYERERERERWYiFFRERERERkYVYWBEREbmg+fPnIywsDAMHDsTEiRNx+/Ztg+MyMjIQGhqKkJAQpKamWjlKIsfGPHMtLKyIiIhcUFxcHHJzc3HmzBn069cPK1as0BsjiiJmz56NPXv2IC8vD5s2bUJeXp4NoiVyTMwz18LCioiIyAXFx8dDoVAAAIYOHYqioiK9MVlZWQgJCUFwcDDc3d0xdepUpKenWztUIofFPHMtLKyIiIhc3Nq1azF27Fi942q1GoGBgY23VSoV1Gq1NUMjchrMM+cnSJJk6xiIHJIgCBmSJI2xdRxERMYIgpAJoLuBU29JkpR+d8xbAKIBPC3d96ZAEITfAUiQJOnFu7f/H4AhkiT90cBzpQBIuXvTQ5KkyNZ7JUT2i3lGDRS2DoDIUbGoIiJ7J0lSrKnzgiA8D2AcgNH3v9m7qwhA4D23VQCKjTxXGoC0BwyVyGExz6gBlwISERG5IEEQxgD4E4BkSZKqjAz7D4C+giD0FgTBHcBUANutFSORo2OeuRYWVkRERK5pFYD2APYLgpAjCMLnACAIQoAgCLsBQJIkLYBXAOwF8COAbyVJOmergIkcEPPMhfAaKyIiIiIiIgtxxoqIiIiIiMhCLKyIiIiIiIgsxMKKiIiIiIjIQiysiIiIiIiILMTCioiIiIiIyEIsrIiIiIiIiCzEwoqIiIiIiMhCLKyIiIiIiIgs9P8BtHvtYUh7FCMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import mglearn\n",
    "# 观察四种数据缩放\n",
    "mglearn.plots.plot_scaling()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四种缩放的特点\n",
    "<ul>\n",
    "    <li>standardscalar确保每个特征的均值为0,方差为1,使所有特征都位于同一数量级，但这种缩放不能保证特征的最大值和最小值</li>\n",
    "    <li>RoubstScalar标准化,公式为x0=(x-Q(50))/(Q(75)-Q(50)).也能确保每个特征的统计属性在同一个范围.会保留异常值的离群特征</li>\n",
    "    <li>MinMaxScaler确保所有特征刚好落到0和1之间，同时保有原来的数据结构</li>\n",
    "    <LI>Normalizer用到一种完全不同的缩放方法,它对每个数据点进行缩放,使得特征向量的欧式长度等于1。当只有数据的方向(或角度)是重要的，<br>\n",
    "    而特征向量的长度无关紧要，那么通常会使用这种归一化</LI>\n",
    "    </ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 应用数据变化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(426, 30)\n",
      "(143, 30)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(cancer.data,cancer.target,random_state=1)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入实现预处理的类\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler() # 实例化预处理类\n",
    "scaler.fit(X_train) # 构建缩放器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缩放后的数据shape:\n",
      "(426, 30)\n",
      "缩放前每个特征的最小值:\n",
      "[6.981e+00 9.710e+00 4.379e+01 1.435e+02 5.263e-02 1.938e-02 0.000e+00\n",
      " 0.000e+00 1.060e-01 5.024e-02 1.153e-01 3.602e-01 7.570e-01 6.802e+00\n",
      " 1.713e-03 2.252e-03 0.000e+00 0.000e+00 9.539e-03 8.948e-04 7.930e+00\n",
      " 1.202e+01 5.041e+01 1.852e+02 7.117e-02 2.729e-02 0.000e+00 0.000e+00\n",
      " 1.566e-01 5.521e-02]\n",
      "缩放前每个特征的最大值:\n",
      "[2.811e+01 3.928e+01 1.885e+02 2.501e+03 1.634e-01 2.867e-01 4.268e-01\n",
      " 2.012e-01 3.040e-01 9.575e-02 2.873e+00 4.885e+00 2.198e+01 5.422e+02\n",
      " 3.113e-02 1.354e-01 3.960e-01 5.279e-02 6.146e-02 2.984e-02 3.604e+01\n",
      " 4.954e+01 2.512e+02 4.254e+03 2.226e-01 9.379e-01 1.170e+00 2.910e-01\n",
      " 5.774e-01 1.486e-01]\n",
      "缩放每个特征的最小值:\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0.]\n",
      "缩放前每个特征的最大值:\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1.]\n"
     ]
    }
   ],
   "source": [
    "# 变换数据\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "# 打印缩放之前和缩放之后的数据\n",
    "print(f\"缩放后的数据shape:\\n{X_train_scaled.shape}\")\n",
    "print(f\"缩放前每个特征的最小值:\\n{X_train.min(axis=0)}\")\n",
    "print(f\"缩放前每个特征的最大值:\\n{X_train.max(axis=0)}\")\n",
    "print(f\"缩放每个特征的最小值:\\n{X_train_scaled.min(axis=0)}\")\n",
    "print(f\"缩放前每个特征的最大值:\\n{X_train_scaled.max(axis=0)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缩放后的特征最小值:\n",
      "[ 0.0336031   0.0226581   0.03144219  0.01141039  0.14128374  0.04406704\n",
      "  0.          0.          0.1540404  -0.00615249 -0.00137796  0.00594501\n",
      "  0.00430665  0.00079567  0.03919502  0.0112206   0.          0.\n",
      " -0.03191387  0.00664013  0.02660975  0.05810235  0.02031974  0.00943767\n",
      "  0.1094235   0.02637792  0.          0.         -0.00023764 -0.00182032]\n",
      "缩放后的特征最大值:\n",
      "[0.9578778  0.81501522 0.95577362 0.89353128 0.81132075 1.21958701\n",
      " 0.87956888 0.9333996  0.93232323 1.0371347  0.42669616 0.49765736\n",
      " 0.44117231 0.28371044 0.48703131 0.73863671 0.76717172 0.62928585\n",
      " 1.33685792 0.39057253 0.89612238 0.79317697 0.84859804 0.74488793\n",
      " 0.9154725  1.13188961 1.07008547 0.92371134 1.20532319 1.63068851]\n"
     ]
    }
   ],
   "source": [
    "# 对测试集数据做相同的缩放\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "# 打印缩放后的数据\n",
    "print(f\"缩放后的特征最小值:\\n{X_test_scaled.min(axis=0)}\")\n",
    "print(f\"缩放后的特征最大值:\\n{X_test_scaled.max(axis=0)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如上,我们会发现测试集缩放后的最大值与最小值不是1和0.这是因为transform方法总是减去训练集的最小值然后除以训练集的范围<br>\n",
    "即MinMaxScaler(以及其他缩放器总是对训练集和测试集数据做相同的变换)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
      "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 对训练集和测试集做相同的缩放\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_blobs\n",
    "import mglearn\n",
    "# 构造数据\n",
    "X,_ = make_blobs(n_samples=50,centers=50,random_state=4,cluster_std=2)\n",
    "# 将其分为训练集和测试集\n",
    "X_train,X_test = train_test_split(X,random_state=5,test_size=0.1)\n",
    "\n",
    "# 绘制训练集和测试集\n",
    "fig,axes = plt.subplots(1,3,figsize=(13,4))\n",
    "axes[0].scatter(X_train[:,0],X_train[:,1],c=mglearn.cm2(0),label='Training set', s=60)\n",
    "axes[0].scatter(X_test[:,0],X_test[:,1],c=mglearn.cm2(1),label='Testing set',s=60,marker='^')\n",
    "axes[0].legend(loc='upper left')\n",
    "axes[0].set_title(\"original Data\")\n",
    "\n",
    "# 利用MinMaxScaler缩放数据\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "# 将正确缩放的数据可视化\n",
    "axes[1].scatter(X_train_scaled[:,0],X_train_scaled[:,1],c=mglearn.cm2(0),label='Training set', s=60)\n",
    "axes[1].scatter(X_test_scaled[:,0],X_test_scaled[:,1],c=mglearn.cm2(1),label='Testing set',s=60,marker='^')\n",
    "axes[1].set_title(\"scaled Data\")\n",
    "\n",
    "# 单独对测试集做缩放\n",
    "# 使得测试集的最小值为0，最大值为1\n",
    "# 千万不要这么做！\n",
    "test_scaler = MinMaxScaler()\n",
    "test_scaler.fit(X_test)\n",
    "X_test_scaled_badly = test_scaler.transform(X_test)\n",
    "axes[2].scatter(X_train_scaled[:,0],X_train_scaled[:,1],c=mglearn.cm2(0),label='Training set', s=60)\n",
    "axes[2].scatter(X_test_scaled_badly[:,0],X_test_scaled_badly[:,1],c=mglearn.cm2(1),label='Testing set',s=60,marker='^')\n",
    "axes[2].set_title(\" Improperly scaled Data\")\n",
    "\n",
    "for ax in axes:\n",
    "    ax.set_xlabel(\"F 0\")\n",
    "    ax.set_ylabel(\"F 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 预处理对监督学习的作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试集精确度:0.9440559440559441\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "X_train,X_test,y_train,y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
    "svm = SVC(C=100)\n",
    "svm.fit(X_train,y_train)\n",
    "print(f\"测试集精确度:{svm.score(X_test,y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缩放后,测试集精确度:0.965034965034965\n"
     ]
    }
   ],
   "source": [
    "# 对数据缩放\n",
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled =  scaler.transform(X_test)\n",
    "# 在缩放后的数据集上学习\n",
    "svm.fit(X_train_scaled,y_train)\n",
    "print(f\"缩放后,测试集精确度:{svm.score(X_test_scaled,y_test)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缩放后的数据集:0.958041958041958\n"
     ]
    }
   ],
   "source": [
    "# 利用零均值和单位方差做缩放\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train_scaled = scaler.transform(X_train)\n",
    "X_test_scaled  = scaler.transform(X_test)\n",
    "\n",
    "# 学习模型\n",
    "svm.fit(X_train_scaled,y_train)\n",
    "print(f\"缩放后的数据集:{svm.score(X_test_scaled,y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结,数据缩放步骤\n",
    "<OL>\n",
    "    <LI>导入预处理类:from sklearn.preprocessing import MinMaxScaler</LI>\n",
    "    <li>实例化预处理类:scaler = MinMaxScaler()</li>\n",
    "    <LI>构造缩放器:scaler.fit(X_train)</LI>\n",
    "    <LI>缩放数据:X_train_scaled=scaler.transform(X_train)</LI>\n",
    "</OL>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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